Jiri Mekyska

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics… Zobrazit více

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications. Zobrazit méně

Speech disorders, collectively referred to as hypokinetic dysarthria (HD), are early biomarkers of Parkinson’s disease (PD). To assess all dimensions of HD, patients could perform several speech tasks using a smartphone outside a clinic. This paper aims to adapt the parametrization process to running speech so that a patient is not required to interact actively with the device, and features can be extracted directly from phone calls. The method utilizes a voice activity detector followed by a… Zobrazit více

Speech disorders, collectively referred to as hypokinetic dysarthria (HD), are early biomarkers of Parkinson’s disease (PD). To assess all dimensions of HD, patients could perform several speech tasks using a smartphone outside a clinic. This paper aims to adapt the parametrization process to running speech so that a patient is not required to interact actively with the device, and features can be extracted directly from phone calls. The method utilizes a voice activity detector followed by a voicing detection. The algorithm was tested on a database of 126 recordings (86 patients with PD and 40 healthy controls) of monologue mixed with noise with different signal-to-noise ratios (SNR) to simulate the real environment conditions. Pearson correlation coefficients show a strong linear relationship between speech features and patients’ scores assessing HD and other motor/non-motor symptoms – p-value < 0.01 for the normalized amplitude quotient (NAQ) with Test 3F Dysarthric Profile (DX index) and Unified Parkinson’s Disease Rating Scale (part III) in 20 dB SNR conditions, p-value < 0.01 for the jitter and shimmer with the Mini Mental State Exam (10 dB SNR). A model based on the Extreme Gradient Boosting algorithm predicts the DX index with a 10.83% estimated error rate (EER) and the Addenbrooke’s Cognitive Examination-Revise (ACE-R) score with 13.38% EER. The introduced algorithm can potentially be used in mHealth applications for passive monitoring and assessment of PD patients. Zobrazit méně

Parkinson’s disease (PD) is a neurodegenerative condition with constantly increasing prevalence rates, affecting strongly life quality in terms of neuromotor and cognitive performance. PD symptoms include voice and speech alterations, known as hypokinetic dysarthria (HD). Unstable phonation is one of the manifestations of HD. Repetitive transcranial magnetic stimulation (rTMS) is a rehabilitative treatment thathas been shown to improve some motor and non-motor symptoms of persons with PD (PwP).… Zobrazit více

Parkinson’s disease (PD) is a neurodegenerative condition with constantly increasing prevalence rates, affecting strongly life quality in terms of neuromotor and cognitive performance. PD symptoms include voice and speech alterations, known as hypokinetic dysarthria (HD). Unstable phonation is one of the manifestations of HD. Repetitive transcranial magnetic stimulation (rTMS) is a rehabilitative treatment thathas been shown to improve some motor and non-motor symptoms of persons with PD (PwP). This study analyzed the phonation functional behavior of 18 participants (13 males, 5 females) with PD diagnosis before (one pre-stimulus) and after (four post-stimulus) evaluation sessions of rTMS treatment, to assess the extent of changes in their phonation stability. Participants were randomized 1:1 to receive either rTMS or sham stimulation. Voice recordings of a sustained vowel [a:] taken immediately before and after the treatment, and at follow-up evaluation sessions (immediately after, at six, ten, and fourteen weeks after the baseline assessment) were processed by inverse filtering to estimate a biomechanical correlate of vocal fold tension. This estimate was further band-pass filtered into EEG-related frequency bands. Log-likelihood ratios (LLRs) between pre- and post-stimulus amplitude distributions of each frequency band showed significant differences in five cases actively stimulated. Seven cases submitted to the sham protocol did not show relevant improvements in phonation instability. Conversely, four active cases did not show phonation improvements, whereas two sham cases did. The study provides early preliminary insights into the capability of phonation quality assessment by monitoring neuromechanical activity from acoustic signals in frequency bands aligned with EEG ones. Zobrazit méně

Handwriting is a complex perceptual–motor skill that is mastered around the age of 8. Although its computerized analysis has been utilized in many biometric and digital health applications, the possible effect of gender is frequently neglected. The aim of this paper is to analyze different online handwritten tasks performed by intact subjects and explore gender differences in commonly used temporal, kinematic, and dynamic features. The differences were explored in the BIOSECUR-ID database. We… Zobrazit více

Handwriting is a complex perceptual–motor skill that is mastered around the age of 8. Although its computerized analysis has been utilized in many biometric and digital health applications, the possible effect of gender is frequently neglected. The aim of this paper is to analyze different online handwritten tasks performed by intact subjects and explore gender differences in commonly used temporal, kinematic, and dynamic features. The differences were explored in the BIOSECUR-ID database. We have identified a significant gender difference in on-surface/in-air time of genuine and skilled forgery signatures, on-surface time in cursive letters and numbers, and pressure, speed, and acceleration in text written in capital letters. Our findings accent the need to consider gender as an important confounding factor in studies dealing with online handwriting signal processing. Zobrazit méně

Parkinson’s disease (PD) is a common neurodegenerative disorder with a prevalence rate estimated to 2.0% for people aged over 65 years. Cardinal motor symptoms of PD such as rigidity and bradykinesia affect the muscles involved in the handwriting process resulting in handwriting abnormalities called PD dysgraphia. Nowadays, online handwritten signal (signal with temporal information) acquired by the digitizing tablets is the most advanced approach of graphomotor difficulties analysis. Although… Zobrazit více

Parkinson’s disease (PD) is a common neurodegenerative disorder with a prevalence rate estimated to 2.0% for people aged over 65 years. Cardinal motor symptoms of PD such as rigidity and bradykinesia affect the muscles involved in the handwriting process resulting in handwriting abnormalities called PD dysgraphia. Nowadays, online handwritten signal (signal with temporal information) acquired by the digitizing tablets is the most advanced approach of graphomotor difficulties analysis. Although the basic kinematic features were proved to effectively quantify the symptoms of PD dysgraphia, a recent research identified that the theory of fractional calculus can be used to improve the graphomotor difficulties analysis. Therefore, in this study, we follow up on our previous research, and we aim to explore the utilization of various approaches of fractional order derivative (FD) in the analysis of PD dysgraphia. For this purpose, we used the repetitive loops task from the Parkinson’s disease handwriting database (PaHaW). Handwritten signals were parametrized by the kinematic features employing three FD approximations: Grünwald-Letnikov’s, Riemann-Liouville’s, and Caputo’s. Results of the correlation analysis revealed a significant relationship between the clinical state and the handwriting features based on the velocity. The extracted features by Caputo’s FD approximation outperformed the rest of the analyzed FD approaches. This was also confirmed by the results of the classification analysis, where the best model trained by Caputo’s handwriting features resulted in a balanced accuracy of 79.73% with a sensitivity of 83.78%
and a specificity of 75.68%
. Zobrazit méně

To this date, studies focusing on the prodromal diagnosis of Lewy body diseases (LBDs) based on quantitative analysis of graphomotor and handwriting difficulties are missing. In this work, we enrolled 18 subjects diagnosed with possible or probable mild cognitive impairment with Lewy bodies (MCI-LB), 7 subjects having more than 50% probability of developing Parkinson’s disease (PD), 21 subjects with both possible/probable MCI-LB and probability of PD > 50%, and 37 age- and gender-matched… Zobrazit více

To this date, studies focusing on the prodromal diagnosis of Lewy body diseases (LBDs) based on quantitative analysis of graphomotor and handwriting difficulties are missing. In this work, we enrolled 18 subjects diagnosed with possible or probable mild cognitive impairment with Lewy bodies (MCI-LB), 7 subjects having more than 50% probability of developing Parkinson’s disease (PD), 21 subjects with both possible/probable MCI-LB and probability of PD > 50%, and 37 age- and gender-matched healthy controls (HC). Each participant performed three tasks: Archimedean spiral drawing (to quantify graphomotor difficulties), sentence writing task (to quantify handwriting difficulties), and pentagon copying test (to quantify cognitive decline). Next, we parameterized the acquired data by various temporal, kinematic, dynamic, spatial, and task-specific features. And finally, we trained classification models for each task separately as well as a model for their combination to estimate the predictive power of the features for the identification of LBDs. Using this approach we were able to identify prodromal LBDs with 74% accuracy and showed the promising potential of computerized objective and non-invasive diagnosis of LBDs based on the assessment of graphomotor and handwriting difficulties. Zobrazit méně

Approximately 30–60 % of the time children spend in school is associated with handwriting. However, up to 30 % of them experience handwriting disabilities (HD), which lead to a decrease in their academic performance. Current HD assessment methods are not unified and show signs of subjectivity which can lead to misdiagnosis. The aim of this paper is to propose a new approach to objective HD assessment based on the principle of movement isochrony. For this purpose, we used a database of 137… Zobrazit více

Approximately 30–60 % of the time children spend in school is associated with handwriting. However, up to 30 % of them experience handwriting disabilities (HD), which lead to a decrease in their academic performance. Current HD assessment methods are not unified and show signs of subjectivity which can lead to misdiagnosis. The aim of this paper is to propose a new approach to objective HD assessment based on the principle of movement isochrony. For this purpose, we used a database of 137 children attending a primary school, who performed a transcription and dictation task, and who were associated with a BHK (Concise Evaluation Scale for Children's Handwriting) score. Employing a machine learning model, we were able to estimate this score with 18 % error. An interpretation of the model suggests that the isochrony-based features could bring new benefits to the objective assessment of HD. Zobrazit méně

This paper is devoted to the computerized auto-mated diagnosis of the prodromal state of Lewy body diseases (LBD) based on actigraphy. LBD is a group of neurodegenerative diseases that require early treatment to alleviate the course of the disease and improve the quality of the lives of patients. This work proposes a method of prodromal diagnosis of LBD based on quantitative analysis of actigraphic sleep data. A new method of sleep and wake detection based on the XGBoost classifier and the… Zobrazit více

This paper is devoted to the computerized auto-mated diagnosis of the prodromal state of Lewy body diseases (LBD) based on actigraphy. LBD is a group of neurodegenerative diseases that require early treatment to alleviate the course of the disease and improve the quality of the lives of patients. This work proposes a method of prodromal diagnosis of LBD based on quantitative analysis of actigraphic sleep data. A new method of sleep and wake detection based on the XGBoost classifier and the angle of the z-axis is introduced, which achieves 83 % accuracy and surpasses the results of state-of-the-art methods. Furthermore, a method that can distinguish subjects with prodromal LBD (50 subjects with Parkinson's disease, dementia with Lewy bodies or mild cognitive impairment) and healthy controls (63 subjects) with 94 % accuracy was introduced. The sensitivity of the method of 100 % and specificity of 91 % was considered sufficient for clinical practice and the proposed methods can help develop decision-making tools that maximize the potential for an early and objective diagnosis of LBD. Zobrazit méně

Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides… Zobrazit více

Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL—0.65 (HF), 0.58 (CNN); LOLO—0.65 (HF), 0.57 (CNN); and ALC—0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL—0.66 (HF), 0.62 (CNN); LOLO—0.56 (HF), 0.54 (CNN); and ALC—0.60 (HF), 0.60 (CNN). Zobrazit méně

Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive technique which is known to produce modifications in cortical brain activity. This paper is devoted to describe potential beneficial effects of rTMS on the phonation stability of Parkinson’s Disease Patients (PDPs). To this end, several measurements derived from phonation have been studied. The stability of phonation is evaluated on sustained emisions of certain open vowels, as [a:]. Using vocal tract inversion, a correlate… Zobrazit více

Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive technique which is known to produce modifications in cortical brain activity. This paper is devoted to describe potential beneficial effects of rTMS on the phonation stability of Parkinson’s Disease Patients (PDPs). To this end, several measurements derived from phonation have been studied. The stability of phonation is evaluated on sustained emisions of certain open vowels, as [a:]. Using vocal tract inversion, a correlate of the glottal source (pressure on the supraglottal rim of the vocal folds) is estimated from vowel emissions. The glottal source power spectral density is used to indirectly estimate the biomechanical tension of the vocal folds. The neuromotor instabilities experienced by PDPs, affecting the vocal fold tension are used as perturbation features related to tremor bands. A longitudinal analysis of the features from an active rTMS case can be compared in different time laps after rTMS, and tested against those from a similar study on a sham rTMS case. Relevant improvements on phonation stability may be appreciated on the active rTMS case compared to the sham one, which are reflected on several features as biomechanical tremor bands. These results open a new non-invasive, costless and remote methodology for PD functional neuromotor evaluation. Zobrazit méně

Introduction
Impaired copy of intersecting pentagons from the Mini-Mental State Examination (MMSE), has been used to assess dementia in Parkinson's disease (PD). We used a digitizing tablet during the pentagon copying test (PCT) as a potential tool for evaluating early cognitive deficits in PD without major cognitive impairment. We also aimed to uncover the neural correlates of the identified parameters using whole-brain magnetic resonance imaging (MRI).

Methods
We enrolled 27… Zobrazit více

Introduction
Impaired copy of intersecting pentagons from the Mini-Mental State Examination (MMSE), has been used to assess dementia in Parkinson's disease (PD). We used a digitizing tablet during the pentagon copying test (PCT) as a potential tool for evaluating early cognitive deficits in PD without major cognitive impairment. We also aimed to uncover the neural correlates of the identified parameters using whole-brain magnetic resonance imaging (MRI).

Methods
We enrolled 27 patients with PD without major cognitive impairment and 25 age-matched healthy controls (HC). We focused on drawing parameters using a digitizing tablet. Parameters with between-group differences were correlated with cognitive outcomes and were used as covariates in the whole-brain voxel-wise analysis using voxel-based morphometry; familywise error (FWE) threshold p < 0.001.

Results
PD patients differed from HC in attention domain z-scores (p < 0.0001). In terms of tablet parameters, the groups differed in Shannon entropy (horizontal in-air, p = 0.003), which quantifies the movements between two strokes. In PD, a correlation was found between the median of Shannon entropy (horizontal in-air) and attention z-scores (R = −0.55, p = 0.006). The VBM revealed an association between our drawing parameter of interest and gray matter (GM) volume variability in the right superior parietal lobe (SPL).

Conclusion
Using a digitizing tablet during the PCT, we identified a novel entropy-based parameter that differed between the nondemented PD and HC groups. This in-air parameter correlated with the level of attention and was linked to GM volume variability of the region engaged in spatial attention. Zobrazit méně

Advantageous property of behavioural signals (e.g. handwriting), in contrast to morphological ones (e.g. iris, fingerprint, hand geometry), is the possibility to ask a user to perform many different tasks. This article summarises recent findings and applications of different handwriting/drawing tasks in the field of security and health. More specifically, it is focused on on-line handwriting and hand-based interaction, i.e. signals that utilise a digitizing device (specific devoted or… Zobrazit více

Advantageous property of behavioural signals (e.g. handwriting), in contrast to morphological ones (e.g. iris, fingerprint, hand geometry), is the possibility to ask a user to perform many different tasks. This article summarises recent findings and applications of different handwriting/drawing tasks in the field of security and health. More specifically, it is focused on on-line handwriting and hand-based interaction, i.e. signals that utilise a digitizing device (specific devoted or general-purpose tablet/smartphone) during the realization of the tasks. Such devices permit the acquisition of on-surface dynamics as well as in-air movements in time, thus providing complex and richer information when compared to the conventional “pen and paper” method. Although the scientific literature reports a wide range of tasks and applications, in this paper, we summarize only those providing competitive results (e.g. in terms of discrimination power) and having a significant impact in the field. Zobrazit méně

As the popularity of decentralised clinical trials increases, there is a need to have a tool enabling remote assessment of sleep, while having good consistency with the golden standard, i.e. with polysomnography (PSG). This study aims to introduce a new approach to sleep assessment that utilises the modelling of actigraphy data by a gradient boosting algorithm. The method is compared to a conventional baseline technique in terms of sleep/wake stages detection accuracy in a dataset containing 55… Zobrazit více

As the popularity of decentralised clinical trials increases, there is a need to have a tool enabling remote assessment of sleep, while having good consistency with the golden standard, i.e. with polysomnography (PSG). This study aims to introduce a new approach to sleep assessment that utilises the modelling of actigraphy data by a gradient boosting algorithm. The method is compared to a conventional baseline technique in terms of sleep/wake stages detection accuracy in a dataset containing 55 recordings of actigraphy and PSG (acquired from 28 subjects). In addition, we explored how well the outputs of the new method agree with data acquired via sleep diaries in another dataset including 150 recordings (22 subjects). With 97% sensitivity and 73%specificity, the new method significantly outperformed the baseline one in modelling the PSG ground truth. On the other hand, it had a lower agreement with the patient-reported outcomes. The results suggest that a combination of both approaches could be a good alternative to the golden standard in remote sleep assessment studies. Zobrazit méně

Parkinson's disease (PD) is associated with several speech/voice disorders collectively referred to as hypokinetic dysarthria (HD). The main goal of this study is to identify acoustic features that support the diagnosis of PD while being independent of the language of a speaker. We recorded seven speech (e.g. monologue) and voice (e.g. sustained phonation) tasks in a cohort of 59 PD patients and 44 age-and gender-matched healthy controls (HC) speaking Czech or US English. A non-parametric test… Zobrazit více

Parkinson's disease (PD) is associated with several speech/voice disorders collectively referred to as hypokinetic dysarthria (HD). The main goal of this study is to identify acoustic features that support the diagnosis of PD while being independent of the language of a speaker. We recorded seven speech (e.g. monologue) and voice (e.g. sustained phonation) tasks in a cohort of 59 PD patients and 44 age-and gender-matched healthy controls (HC) speaking Czech or US English. A non-parametric test revealed that the best discrimination power has a measure quantifying the number of interword pauses per minute. In a consequent classification analysis, utilising logistic regression, we observed a drop in the classification accuracy from 72-73% to 67%, when moving from single-language modelling to the multilingual one. The results of this study suggest that especially the prosodic (pause-based) features could play a significant role in the automatic language-independent diagnosis of PD. Zobrazit méně

Objectives
We evaluated the long-term effects of multiple-session repetitive transcranial magnetic stimulation on hypokinetic dysarthria in PD. Neural mechanisms of stimulation were assessed by functional MRI.

Methods
A randomized parallel-group sham stimulation-controlled design was used. Patients were randomly assigned to ten sessions (2 weeks) of real (1 Hz) or sham stimulation over the right superior temporal gyrus. Stimulation effects were evaluated at weeks 2, 6, and 10 after… Zobrazit více

Objectives
We evaluated the long-term effects of multiple-session repetitive transcranial magnetic stimulation on hypokinetic dysarthria in PD. Neural mechanisms of stimulation were assessed by functional MRI.

Methods
A randomized parallel-group sham stimulation-controlled design was used. Patients were randomly assigned to ten sessions (2 weeks) of real (1 Hz) or sham stimulation over the right superior temporal gyrus. Stimulation effects were evaluated at weeks 2, 6, and 10 after the baseline assessment. Articulation, prosody, and speech intelligibility were quantified by speech therapist using a validated tool (Phonetics score of the Dysarthric Profile). Activations of the speech network regions and intrinsic connectivity were assessed using 3T MRI. Linear mixed models and post-hoc tests were utilized for data analyses.

Results
Altogether 33 PD patients completed the study (20 in the real stimulation group and 13 in the sham stimulation group). Linear mixed models revealed significant effects of time (F(3, 88.1) = 22.7, p < 0.001) and time-by-group interactions: F(3, 88.0) = 2.8, p = 0.040) for the Phonetics score. Real as compared to sham stimulation led to activation increases in the orofacial sensorimotor cortex and caudate nucleus and to increased intrinsic connectivity of these regions with the stimulated area.

Conclusions
This is the first study to show the long-term treatment effects of non-invasive brain stimulation for hypokinetic dysarthria in PD. Neural mechanisms of the changes are discussed. Zobrazit méně

Writing is a complex skill. Issues in this process, which are usually associated with developmental dysgraphia (DD), could consistently cause problems in everyday life, like for example, lower self-esteem and poorer academic achievement. That is why the correct diagnosis of DD is crucial for further child development. DD belongs to the category of specific learning disabilities and according to different studies, its prevalence ranges between 0.1 and 30 percent. Diagnosing a child with DD… Zobrazit více

Writing is a complex skill. Issues in this process, which are usually associated with developmental dysgraphia (DD), could consistently cause problems in everyday life, like for example, lower self-esteem and poorer academic achievement. That is why the correct diagnosis of DD is crucial for further child development. DD belongs to the category of specific learning disabilities and according to different studies, its prevalence ranges between 0.1 and 30 percent. Diagnosing a child with DD relies, in the first place, on teachers. After that, psychologists, or special educational specialists (in the Czech Republic) commonly use qualitative evaluation of the written process, where the child is observed when he or she is writing. Nevertheless, there are no objective tests or standardized examinations for the assessment of handwriting deficiency either in special educational or psychological practices. In the frame of current research, a new quantitative approach to handwriting proficiency assessment was developed. Digitizing tablets (Wacom Intuos Pro L) with a special inking pen (Wacom Ink Pen) are used to record the online handwriting process and graphomotor skills of children. Administration templates contain simple graphomotor elements and complex figures related to DD symptoms and cognitive (memory and visuospatial) abilities. This new approach to diagnose handwriting issues will be presented in this article. Zobrazit méně

Introduction
Hypokinetic dysarthria (HD) is common in Parkinson’s disease (PD). Our objective was to evaluate articulatory networks and their reorganization due to PD pathology in individuals without overt speech impairment using a multimodal MRI protocol and acoustic analysis of speech.

Methods
A total of 34 PD patients with no subjective HD complaints and 25 age-matched healthy controls (HC) underwent speech task recordings, structural MRI, and reading task-induced and… Zobrazit více

Introduction
Hypokinetic dysarthria (HD) is common in Parkinson’s disease (PD). Our objective was to evaluate articulatory networks and their reorganization due to PD pathology in individuals without overt speech impairment using a multimodal MRI protocol and acoustic analysis of speech.

Methods
A total of 34 PD patients with no subjective HD complaints and 25 age-matched healthy controls (HC) underwent speech task recordings, structural MRI, and reading task-induced and resting-state fMRI. Grey matter probability maps, task-induced activations, and resting-state functional connectivity within the regions engaged in speech production (ROIs) were assessed and compared between groups. Correlation with acoustic parameters was also performed.

Results
PD patients as compared to HC displayed temporal decreases in speech loudness which were related to BOLD signal increases in the right-sided regions of the dorsal language pathway/articulatory network. Among those regions, activation of the right anterior cingulate was increased in PD as compared to HC. We also found bilateral posterior superior temporal gyrus (STG) GM loss in PD as compared to HC that was strongly associated with diadochokinetic (DDK) irregularity in the PD group. Task-induced activations of the left STG were increased in PD as compared to HC and were related to the DDK rate control.

Conclusions
The results provide insight into the neural correlates of speech production control and distinct articulatory network reorganization in PD apparent already in patients without subjective speech impairment. Zobrazit méně

Background
We aimed to confirm the “Mozart effect” in epileptic patients using the intracerebral EEG recordings and the hypothesis that the reduction of epileptiform discharges (ED) can be explained by the music’s acoustic properties.

Methods
Eighteen epilepsy surgery candidates were implanted with depth electrodes in the temporal medial and lateral cortex. Patients listened to the first movement of Mozart’s Sonata for Two Pianos K. 448 and to the first movement of Haydn’s Symphony… Zobrazit více

Background
We aimed to confirm the “Mozart effect” in epileptic patients using the intracerebral EEG recordings and the hypothesis that the reduction of epileptiform discharges (ED) can be explained by the music’s acoustic properties.

Methods
Eighteen epilepsy surgery candidates were implanted with depth electrodes in the temporal medial and lateral cortex. Patients listened to the first movement of Mozart’s Sonata for Two Pianos K. 448 and to the first movement of Haydn’s Symphony No. 94. Musical features from each composition with respect to rhythm, melody, and harmony were analysed.

Results
ED in intracerebral EEG were reduced by Mozart’s music. Listening to Haydn’s music led to reduced ED only in the women; in the men, the ED increased.

The acoustic analysis revealed that non‐dissonant music with a harmonic spectrum and decreasing tempo with significant high‐frequency parts has a reducing effect on ED in men. To reduce ED in women, the music should additionally be, in terms of loudness, gradually less dynamic. Finally, we were able to demonstrate that these acoustic characteristics are more dominant in Mozart’s music than in Haydn’s music.

Conclusions
We confirmed the reduction of intracerebral ED while listening to classical music. An analysis of the musical features revealed that the acoustic characteristics of music are responsible for supressing brain epileptic activity. Based on our study we suggest to study the use of musical pieces with well‐defined acoustic properties as an alternative non‐invasive method to reduce epileptic activity in patients with epilepsy. Zobrazit méně

Graphomotor disabilities (GD) are present in up to 30% of school-aged children and are associated with several symptoms in the field of kinematics. Although the basic kinematic features such as velocity, acceleration, and jerk were proved to effectively quantify these symptoms, a recent body of research identified that the theory of fractional calculus can be used to even improve the objective GD assessment. The goal of this study is to extend the current knowledge in this field and explore the… Zobrazit více

Graphomotor disabilities (GD) are present in up to 30% of school-aged children and are associated with several symptoms in the field of kinematics. Although the basic kinematic features such as velocity, acceleration, and jerk were proved to effectively quantify these symptoms, a recent body of research identified that the theory of fractional calculus can be used to even improve the objective GD assessment. The goal of this study is to extend the current knowledge in this field and explore the abilities of several fractional order derivatives (FD) approximations to estimate the severity of GD in the children population. We enrolled 85 children attending the 3rd and 4th grade of primary school, who performed a combined loop task on a digitizing tablet. Their performance was rated by psychologists and the online handwriting signals were parametrised by kinematic features utilising three FD approximations: Grünwald-Letnikov’s, Riemann–Liouville’s, and Caputo’s. In this study, we showed the differences across the employed FD approaches for the same kinematic handwriting features and their potential in GD analysis. The results suggest that the Riemann-Liouville’s approximation in the field of quantitative GD analysis outperforms the other ones. Using this approach, we were able to estimate the overall score with a low error of 0.65 points, while the scale range is 4. In fact, the psychologists tend to make the error even higher. Zobrazit méně

Background
Diffusion kurtosis imaging has been applied to evaluate white matter and basal ganglia microstructure in mixed Parkinson's disease (PD) groups with inconclusive results.

Objectives
To evaluate specific patterns of kurtosis changes in PD and to assess the utility of diffusion imaging in differentiating between healthy subjects and cognitively normal PD, and between PD with and without mild cognitive impairment.

Methods
Diffusion scans were obtained in 92… Zobrazit více

Background
Diffusion kurtosis imaging has been applied to evaluate white matter and basal ganglia microstructure in mixed Parkinson's disease (PD) groups with inconclusive results.

Objectives
To evaluate specific patterns of kurtosis changes in PD and to assess the utility of diffusion imaging in differentiating between healthy subjects and cognitively normal PD, and between PD with and without mild cognitive impairment.

Methods
Diffusion scans were obtained in 92 participants using 3T MRI. Differences in white matter were tested by tract-based spatial statistics. Gray matter was evaluated in basal ganglia, thalamus, hippocampus, and motor and premotor cortices. Brain atrophy was also assessed. Multivariate logistic regression was used to identify a combination of diffusion parameters with the highest discrimination power between groups.

Results
Diffusion kurtosis metrics showed a significant increase in substantia nigra (p = 0.037, Hedges' g = 0.89), premotor (p = 0.009, Hedges' g = 0.85) and motor (p = 0.033, Hedges' g = 0.87) cortices in PD with normal cognition compared to healthy participants. Combined diffusion markers in gray matter reached 81% accuracy in differentiating between both groups. Significant white matter microstructural changes, and kurtosis decreases in the cortex were present in cognitively impaired versus cognitively normal PD. Diffusion parameters from white and gray matter differentiated between both PD phenotypes with 78% accuracy.

Conclusions
Increased kurtosis in gray matter structures in cognitively normal PD reflects increased hindrance to water diffusion caused probably by alpha-synuclein-related microstructural changes. In cognitively impaired PD, the changes are mostly driven by decreased white matter integrity. Our results support the utility of diffusion kurtosis imaging for PD diagnostics. Zobrazit méně

Speech is controlled by axial neuromotor systems, therefore, it is highly sensitive to the effects of neurodegenerative illnesses such as Parkinson’s Disease (PD). Patients suffering from PD present important alterations in speech, which are manifested in phonation, articulation, prosody, and fluency. These alterations may be evaluated using statistical methods on features obtained from glottal, spectral, cepstral, or fractal descriptions of speech. This work introduces an evaluation paradigm… Zobrazit více

Speech is controlled by axial neuromotor systems, therefore, it is highly sensitive to the effects of neurodegenerative illnesses such as Parkinson’s Disease (PD). Patients suffering from PD present important alterations in speech, which are manifested in phonation, articulation, prosody, and fluency. These alterations may be evaluated using statistical methods on features obtained from glottal, spectral, cepstral, or fractal descriptions of speech. This work introduces an evaluation paradigm based on Information Theory (IT) to differentiate the effects of PD and aging on glottal amplitude distributions. The study is conducted on a database including 48 PD patients (24 males, 24 females), 48 age-matched healthy controls (HC, 24 males, 24 females), and 48 mid-age normative subjects (NS, 24 males, 24 females). It may be concluded from the study that Hierarchical Clustering (HiCl) methods produce a clear separation between the phonation of PD patients from NS subjects (accuracy of 89.6% for both male and female subsets), but the separation between PD patients and HC subjects is less efficient (accuracy of 75.0% for the male subset and 70.8% for the female subset). Conversely, using feature selection and Support Vector Machine (SVM) classification, the differentiation between PD and HC is substantially improved (accuracy of 94.8% for the male subset and 92.8% for the female subset). This improvement was mainly boosted by feature selection, at a cost of information and generalization losses. The results point to the possibility that speech deterioration may affect HC phonation with aging, reducing its difference to PD phonation. Zobrazit méně

The objective of this paper is to present the state of-the-art relating to automatic speech and voice analysis techniques as applied to the monitoring of patients suffering from Alzheimer's disease as well as to shed light on possible future research topics. This work reviews more than 90 papers in the existing literature and focuses on the main feature extraction techniques and classification methods used. In order to guide researchers interested in working in this area, the most frequently… Zobrazit více

The objective of this paper is to present the state of-the-art relating to automatic speech and voice analysis techniques as applied to the monitoring of patients suffering from Alzheimer's disease as well as to shed light on possible future research topics. This work reviews more than 90 papers in the existing literature and focuses on the main feature extraction techniques and classification methods used. In order to guide researchers interested in working in this area, the most frequently used data repositories are also given. Likewise, it identifies the most clinically relevant results and the current lines developed in the field. Automatic speech analysis, within the Health 4.0 framework, offers the possibility of assessing these patients, without the need for a specific infrastructure, by means of non-invasive, fast and inexpensive techniques as a complement to the current diagnostic methods. Zobrazit méně

School-aged children spend 31-60% of their time at school performing handwriting, which is a complex perceptual-motor skill composed of a coordinated combination of fine graphomotor movements. As up to 30% of them experience graphomotor difficulties (GD), timely diagnosis of these difficulties and therapeutic intervention are of great importance. At present, an objective, computerized decision support system for the identification and assessment of GD in school-aged children is still missing… Zobrazit více

School-aged children spend 31-60% of their time at school performing handwriting, which is a complex perceptual-motor skill composed of a coordinated combination of fine graphomotor movements. As up to 30% of them experience graphomotor difficulties (GD), timely diagnosis of these difficulties and therapeutic intervention are of great importance. At present, an objective, computerized decision support system for the identification and assessment of GD in school-aged children is still missing. In this study, we propose three novel advanced handwriting parametrization techniques based on modulation spectra, fractional order derivatives, and tunable Q-factor wavelet transform to improve the identification of GD using online handwriting. For this purpose, we analyzed signals acquired from 7 basic graphomotor tasks performed by 53 children attending 3rd and 4th grade at several primary schools around the Czech Republic. Combining the newly proposed features with the conventionally used ones, we were able to identify GD with 84% accuracy. In this study, we showed that using advanced parametrization of basic graphomotor movements can be potentially used to improve our capabilities of quantifying problems with the development of legible, fast-paced handwriting, and help with the early diagnosis of handwriting difficulties frequently manifested in developmental dysgraphia. Zobrazit méně

The study of speech and voice in people diagnosed with a neurodegenerative disorder for the purposes of detection and monitoring has known a very relevant push forward in these last years, but it is far from being completed. One of the main concerns nowadays is that once the deterioration of speech and phonation quality has been informed by machine learning relying upon clinical expertise, there is insufficient evidence to resolve if quality deterioration may come from organic causes… Zobrazit více

The study of speech and voice in people diagnosed with a neurodegenerative disorder for the purposes of detection and monitoring has known a very relevant push forward in these last years, but it is far from being completed. One of the main concerns nowadays is that once the deterioration of speech and phonation quality has been informed by machine learning relying upon clinical expertise, there is insufficient evidence to resolve if quality deterioration may come from organic causes, neuromotor degeneration or simply from aging. The present work is part of a more ambitious plan to shed light on this problem by resorting to a theoretical modelling of glottal signals under the main known causes affecting phonation quality, which are closure deficits during the phonation cycle. These deficits may be due to anatomical, organic pathologic or neuromotor reasons. Simulation examples explaining them in the glottal excitation signals are given and contrasted with real examples. Finally, relevant scores from an experimental separation of Parkinson Disease phonation samples from 24 male and 24 female subjects against aging 24 male and 24 female controls on the same age taken from a male-female balanced dataset confronted to a normative subset of 24 male and 24 female speakers are presented to exemplify an analysis study deepening into this problem. Although classification accuracy scores as high as 99.69 and 99.59 were attained in 10-fold cross-validation using an SVM classifier, there is still the impression that co-morbidity and aging effects are not well taken into account, requiring a further semantic study on the features behind the discrimination scores obtained. Zobrazit méně

Dysgraphia (D) is a complex specific learning disorder with a prevalence of up to 30%, which is linked with handwriting issues. The factors recognized for assessing these issues are legibility and performance time. Two questionnaires, the Handwriting Proficiency Screening Questionnaire (HPSQ) for teachers and its modification for children (HPSQ-C), were established as quick and valid screening tools along with a third factor – emotional and physical well-being. Until now, in the Czechia, there… Zobrazit více

Dysgraphia (D) is a complex specific learning disorder with a prevalence of up to 30%, which is linked with handwriting issues. The factors recognized for assessing these issues are legibility and performance time. Two questionnaires, the Handwriting Proficiency Screening Questionnaire (HPSQ) for teachers and its modification for children (HPSQ-C), were established as quick and valid screening tools along with a third factor – emotional and physical well-being. Until now, in the Czechia, there has been no validated screening tool for D diagnosis. A study was conducted on a set of 294 children from 3rd and 4th year of primary school (132 girls/162 boys; Mage 8.96 ± 0.73) and 21 teachers who spent most of their time with them. Confirmatory factor analysis based on the theoretical background showed poor fit for HPSQ [χ2(32) = 115.07, p < 0.001; comparative fit index (CFI) = 0.95; Tucker–Lewis index (TLI) = 0.93; root mean square error of approximation (RMSEA) = 0.09; standard root mean square residual (SRMR) = 0.05] and excellent fit for HPSQ-C [χ2(32) = 31.12, p = 0.51; CFI = 1.0; TLI = 1.0; RMSEA = 0.0; SRMR = 0.04]. For the HPSQ-C models, there were no differences between boys and girls [Δχ2(7) = 12.55, p = 0.08]. Values of McDonalds’s ω indicate excellent (HPSQ, ω = 0.9) and acceptable (HPSQ-C, ω = 0.7) reliability. Boys were assessed as worse writers than girls based on the results of both questionnaires. The grades positively correlate with the total scores of both HPSQ (r = 0.54, p < 0.01) and HPSQ-C (r = 0.28, p < 0.01). Based on the results, for the assessment of handwriting difficulties experienced by Czech children, we recommend using the HPSQ-C questionnaire for research purposes. Zobrazit méně

Although graphomotor difficulties (GD) are present in up to 30% of school-aged children, the field of GD diagnosis and assessment is not fully explored and several research gaps can be identified. This study aims to explore the impact of specific elementary graphomotor tasks analysis on the accuracy of computerised diagnosis and assessment of GD. We analysed seven basic graphomotor tasks from 76 children (assessed by special educational counsellors and using the handwriting proficiency… Zobrazit více

Although graphomotor difficulties (GD) are present in up to 30% of school-aged children, the field of GD diagnosis and assessment is not fully explored and several research gaps can be identified. This study aims to explore the impact of specific elementary graphomotor tasks analysis on the accuracy of computerised diagnosis and assessment of GD. We analysed seven basic graphomotor tasks from 76 children (assessed by special educational counsellors and using the handwriting proficiency screening questionnaire for children HPSQ-C). Employing a differential analysis, we observed that the most discriminative tasks are based on combined loops, sawtooth and small Archimedean spiral drawings. Features with the highest discrimination power quantify kinematics, especially in the vertical projection. Using a multivariate mathematical model, we were able to identify GD with 50% sensitivity and 90% specificity, and to estimate the total score of HPSQ-C with 31% error. Zobrazit méně

Handwriting difficulties (HD) affects some of the school-aged children and its current prevalence rate is between 5-34%. Children at primary schools have to face rising cognitive demands that the handwriting represents, and some of them are not able to do so. As a result, they tend to make mistakes and their written product is dysfluent and has poor legibility. HD can also lead them to lower self-esteem, learning difficulties and ultimately to less academic achievements. For this reason… Zobrazit více

Handwriting difficulties (HD) affects some of the school-aged children and its current prevalence rate is between 5-34%. Children at primary schools have to face rising cognitive demands that the handwriting represents, and some of them are not able to do so. As a result, they tend to make mistakes and their written product is dysfluent and has poor legibility. HD can also lead them to lower self-esteem, learning difficulties and ultimately to less academic achievements. For this reason occupational therapists are trying to identify HD through examination as early as possible. We extracted online handwriting signals of children using digitizing tablets. Handwriting Proficiency Screening Questionnaire for Children (HPSQ-C) was used to score severity of HD in children's written product. To advance current computerized analysis of online handwriting, we employed fractional order derivative features (FD) together with conventional measures. We selected significant features for HD identification and utilized correlation analysis together with Mann-Whitney U-test to evaluate their discriminative power. We can conclude that FD-based features bring benefits of more robust quantification of in-air movements as opposed to the conventionally used ones. Finally, we have shown that utilization of FD can be beneficial for computerized assessment of HD but should be further optimized and evaluated with advanced statistical or machine learning methods. Zobrazit méně

Speech is a vehicular tool to detect neurological degeneration using certain accepted biomarkers derived from sustained vowels, diadochokinetic exercises, or running speech. Classically, mel-frequency cepstral coefficients (MFCCs) have been used in the organic and neurologic characterization of pathologic phonation using sustained vowels. In the present paper, a comparative study has been carried on comparing Parkinson’s disease detection results using MFCCs and vowel articulation kinematic… Zobrazit více

Speech is a vehicular tool to detect neurological degeneration using certain accepted biomarkers derived from sustained vowels, diadochokinetic exercises, or running speech. Classically, mel-frequency cepstral coefficients (MFCCs) have been used in the organic and neurologic characterization of pathologic phonation using sustained vowels. In the present paper, a comparative study has been carried on comparing Parkinson’s disease detection results using MFCCs and vowel articulation kinematic distributions derived from the first two formants. Binary classification results using support vector machines avail the superior performance of articulation kinematic distributions with respect to MFCCs regarding sensitivity, specificity, and accuracy. The fusion of both types of features could lead to improve general performance in PD detection and monitoring from speech. Zobrazit méně

Speech is a vehicular tool to detect neurological degeneration using certain accepted biomarkers derived from sustained vowels, diadochokinetic exercises, or running speech. Classically, mel-frequency cepstral coefficients (MFCCs) have been used in the organic and neurologic characterization of pathologic phonation using sustained vowels. In the present paper, a comparative study has been carried on comparing Parkinson’s disease detection results using MFCCs and vowel articulation kinematic… Zobrazit více

Speech is a vehicular tool to detect neurological degeneration using certain accepted biomarkers derived from sustained vowels, diadochokinetic exercises, or running speech. Classically, mel-frequency cepstral coefficients (MFCCs) have been used in the organic and neurologic characterization of pathologic phonation using sustained vowels. In the present paper, a comparative study has been carried on comparing Parkinson’s disease detection results using MFCCs and vowel articulation kinematic distributions derived from the first two formants. Binary classification results using support vector machines avail the superior performance of articulation kinematic distributions with respect to MFCCs regarding sensitivity, specificity, and accuracy. The fusion of both types of features could lead to improve general performance in PD detection and monitoring from speech. Zobrazit méně

Parkinson's disease (PD) is a common neurodegenerative disorder with prevalence rate estimated to 1.5 % for people age over 65 years. The majority of PD patients is associated with handwriting abnormalities called PD dysgraphia, which is linked with rigidity and bradykinesia of muscles involved in the handwriting process. One of the effective approaches of quantitative PD dysgraphia analysis is based on online handwriting processing. In the frame of this study we aim to deeply evaluate and… Zobrazit více

Parkinson's disease (PD) is a common neurodegenerative disorder with prevalence rate estimated to 1.5 % for people age over 65 years. The majority of PD patients is associated with handwriting abnormalities called PD dysgraphia, which is linked with rigidity and bradykinesia of muscles involved in the handwriting process. One of the effective approaches of quantitative PD dysgraphia analysis is based on online handwriting processing. In the frame of this study we aim to deeply evaluate and optimize advanced PD handwriting quantification based on fractional order derivatives (FD). For this purpose, we used 37 PD patients and 38 healthy controls from the PaHaW (PD handwriting database). The FD based features were employed in classification and regression analysis (using gradient boosted trees), and evaluated in terms of their discrimination power and abilities to assess severity of PD. The results suggest that the most discriminative and descriptive information provide FD based features extracted from a repetitive loop task or a sentence copy task (maximum sensitivity/specificity = 76 %, error in severity assessment = 14 %, error in PD duration estimation = 22 %). Next, we identified two optimal ranges for the order of fractional derivative, α = 0.05-0.45 and α = 0.65-0.80. Finally, we observed that inclusion of pressure, azimuth, and tilt together with kinematic features into mathematical modeling has no influence (positive or negative) on classification performance, however, there was a notable improvement in the estimation of PD duration. Zobrazit méně

Speech is a vehicular tool to detect neurological degeneration using certain accepted biomarkers derived from sustained vowels, diadochokinetic exercises or running speech. Classically the Vowel Space Area (VSA) and the Formant Centralization Ratio (FCR) have been proposed to describe dysarthria in Parkinson’s Disease (PD). These features are based in global estimations of the positions of the first two formants in the representation of a vowel triangle. The aim of the paper is to give a… Zobrazit více

Speech is a vehicular tool to detect neurological degeneration using certain accepted biomarkers derived from sustained vowels, diadochokinetic exercises or running speech. Classically the Vowel Space Area (VSA) and the Formant Centralization Ratio (FCR) have been proposed to describe dysarthria in Parkinson’s Disease (PD). These features are based in global estimations of the positions of the first two formants in the representation of a vowel triangle. The aim of the paper is to give a description of speech articulation dynamics as a probability density function of the kinematic features derived from the evolution of formants in the time domain. The statistical distribution of the dynamic behavior of articulation features can be used to estimate differences between speech features from subjects with Parkinson’s dysarthria relative to normative subjects. Utterances of vowels [a:, i:, u:] from a subset of 16 subjects with PD (8 males and 8 females), confronted to a subset of 16 normative subjects (8 males and 8 females) have shown that the statistical distributions of dynamic articulation features can be differentiated using information theory based estimations such as Kullback-Leibler and Jensen-Shannon Divergence (JSD). These estimations allow establishing relevant statistical differences between PD and normative subjects both for males and females, improving the differentiation capability of VSA and FCR. Zobrazit méně

Developmental dysgraphia is a neurodevelopmental disorder present in up to 30% of elementary school pupils. Since it is associated with handwriting difficulties (HD), it has detrimental impact on children's academic progress, emotional well-being, attitude and behaviour. Nowadays, researchers proposed a new approach of HD assessment utilizing digitizing tablets. I.e. that handwriting of children is quantified by a set of conventional parameters, such as velocity, duration of handwriting, tilt… Zobrazit více

Developmental dysgraphia is a neurodevelopmental disorder present in up to 30% of elementary school pupils. Since it is associated with handwriting difficulties (HD), it has detrimental impact on children's academic progress, emotional well-being, attitude and behaviour. Nowadays, researchers proposed a new approach of HD assessment utilizing digitizing tablets. I.e. that handwriting of children is quantified by a set of conventional parameters, such as velocity, duration of handwriting, tilt, etc. The aim of this study is to explore a potential of newly designed online handwriting features based on the tunable Q-factor wavelet transform (TQWT) in terms of computerized HD identification. Using a digitizing tablet, we recorded a written paragraph of 97 children who were also assessed by the Handwriting Proficiency Screening Questionnaire for Children (HPSQ-C). We evaluated discrimination power (binary classification) of all parameters using random forest and support vector machine classifiers in combination with sequential floating forward feature selection. Based on the experimental results we observed that the newly designed features outperformed the conventional ones (accuracy = 79.16%, sensitivity = 86.22%, specificity = 73.32%). When considering the combination of all parameters (including the conventional ones) we reached 84.66% classification accuracy (sensitivity = 88.70%, specificity = 82.53%). The most discriminative parameters were based on vertical movement and pressure, which suggests that children with HD were not able to maintain stable force on pen tip and that their vertical movement is less fluent. The new features we introduced go beyond the state-of-the-art and improve discrimination power of the conventional parameters by approximately 20.0%. Zobrazit méně

Speech is controlled by axial neuromotor systems, highly sensible to certain neurodegenerative illnesses as Parkinson’s Disease (PD). Patients suffering PD present important alterations in speech, which manifest in phonation, articulation, prosody and fluency. Usually phonation and articulation alterations are estimated using different statistical frameworks and methods. The present study introduces a new paradigm based on Information Theory fundamentals to use common statistical tools to… Zobrazit více

Speech is controlled by axial neuromotor systems, highly sensible to certain neurodegenerative illnesses as Parkinson’s Disease (PD). Patients suffering PD present important alterations in speech, which manifest in phonation, articulation, prosody and fluency. Usually phonation and articulation alterations are estimated using different statistical frameworks and methods. The present study introduces a new paradigm based on Information Theory fundamentals to use common statistical tools to differentiate and score PD speech on phonation and articulation estimates. A study describing the performance of a methodology based on this common framework on a database including 16 PD patients, 16 age-paired healthy controls (HC) and 16 mid-age normative subjects (NS) is presented. The results point out to the clear separation between PD patients and HC subjects with respect to NS, but an unclear differentiation between PD and HC. The most important conclusion is that special effort is needed to establish differentiating features between PD, and organic laryngeal, from aging speech. Zobrazit méně

Introduction
Hypokinetic dysarthria (HD) is a common symptom of Parkinson's disease (PD) which does not respond well to PD treatments. We investigated acute effects of repetitive transcranial magnetic stimulation (rTMS) of the motor and auditory feedback area on HD in PD using acoustic analysis of speech.

Methods
We used 10 Hz and 1 Hz stimulation protocols and applied rTMS over the left orofacial primary motor area, the right superior temporal gyrus (STG), and over the vertex (a… Zobrazit více

Introduction
Hypokinetic dysarthria (HD) is a common symptom of Parkinson's disease (PD) which does not respond well to PD treatments. We investigated acute effects of repetitive transcranial magnetic stimulation (rTMS) of the motor and auditory feedback area on HD in PD using acoustic analysis of speech.

Methods
We used 10 Hz and 1 Hz stimulation protocols and applied rTMS over the left orofacial primary motor area, the right superior temporal gyrus (STG), and over the vertex (a control stimulation site) in 16 PD patients with HD. A cross-over design was used. Stimulation sites and protocols were randomised across subjects and sessions. Acoustic analysis of a sentence reading task performed inside the MR scanner was used to evaluate rTMS-induced effects on motor speech. Acute fMRI changes due to rTMS were also analysed.

Results
The 1 Hz STG stimulation produced significant increases of the relative standard deviation of the 2nd formant (p = 0.019), i.e. an acoustic parameter describing the tongue and jaw movements. The effects were superior to the control site stimulation and were accompanied by increased resting state functional connectivity between the stimulated region and the right parahippocampal gyrus. The rTMS-induced acoustic changes were correlated with the reading task-related BOLD signal increases of the stimulated area (R = 0.654, p = 0.029).

Conclusion
Our results demonstrate for the first time that low-frequency stimulation of the temporal auditory feedback area may improve articulation in PD and enhance functional connectivity between the STG and the cortical region involved in an overt speech control. Zobrazit méně

Speech is being considered a pervasive and costless means to detect and monitor neurodegenerative disease progression. Many different approaches have been reported to differentiate normative subject speech from neurodegenerative patient speech. Most of them are focussed on statistical pattern recognition approaches to improve detection results on a baseline, considering only patient speech and normative controls. The definition of a normative control is not well established in itself, usually… Zobrazit více

Speech is being considered a pervasive and costless means to detect and monitor neurodegenerative disease progression. Many different approaches have been reported to differentiate normative subject speech from neurodegenerative patient speech. Most of them are focussed on statistical pattern recognition approaches to improve detection results on a baseline, considering only patient speech and normative controls. The definition of a normative control is not well established in itself, usually being subjects free of any pathology aligned in the same age range as patients. But one question which is not taken into account is the effects of aging in healthy controls, as usually neurodegenerative diseases may include mostly patients affected by certain effects, as dysphonia or dysarthria, as a consequence of aging. The present research introduces a methodology based on information theory to compare the effects produced by aging dysarthria with those due to Parkinson's Disease, using the statistical distribution of speech articulation kinematics as a marker. On the one hand, it may be concluded that articulation kinematics is substantially different for PD and HC with respect to normative subjects. On the other hand, this does not seem to be the case between PD and HC subjects, as these subsets may share some dysarthric features which may be contributed more by aging than by neuromotor degeneration. This differentiation problem needs to be evaluated as well in the case of phonation features, otherwise there will not be full guarantee in using phonation features to assess neuromotor degeneration. In this sense new methodologies have to be designed to distinguish neurodegenerative from aging speech granting better guarantees. Zobrazit méně

Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For… Zobrazit více

Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment. Zobrazit méně

Hypokinetic dysarthria, which is associated with Parkinson’s disease (PD), affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up… Zobrazit více

Hypokinetic dysarthria, which is associated with Parkinson’s disease (PD), affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up. We enrolled 51 PD patients who were assessed by three commonly used clinical scales. In addition, we quantified eight possible phonatory disorders in five vowels. To identify the relationship between baseline phonatory features and changes in clinical scores, we performed a partial correlation analysis. Finally, we trained XGBoost models to predict the changes in clinical scores during a two-year follow-up. For two years, the patients’ voices became more aperiodic with increased microperturbations of frequency and amplitude. Next, the XGBoost models were able to predict changes in clinical scores with an error in range 11–26%. Although we identified some significant correlations between changes in phonatory features and clinical scores, they are less interpretable. This study suggests that it is possible to predict the progress of PD based on the acoustic analysis of phonation. Moreover, it recommends utilizing the sustained vowel /i/ instead of /a/. Zobrazit méně

Parkinson's disease (PD) is one of the most frequent neurodegenerative disorder with progressive decline in several motor and non-motor skills. Due to time-consuming and partially subjective conventional PD diagnosis, several more effective approaches based on signal processing and machine learning, e. g. online handwriting analysis, have been proposed. This paper introduces a new methodology of PD dysgraphia analysis based on fractional derivatives applied in PD handwriting quantification. The… Zobrazit více

Parkinson's disease (PD) is one of the most frequent neurodegenerative disorder with progressive decline in several motor and non-motor skills. Due to time-consuming and partially subjective conventional PD diagnosis, several more effective approaches based on signal processing and machine learning, e. g. online handwriting analysis, have been proposed. This paper introduces a new methodology of PD dysgraphia analysis based on fractional derivatives applied in PD handwriting quantification. The proposed methodology was evaluated on a database that consists 33 PD patients and 36 healthy controls who performed several handwriting tasks. Employing random forests classifier in combination with 5 kinematic features based on fractional-order derivatives we reached 90% classification accuracy, 89% sensitivity, and 91% specificity. In comparison with the results of other related works dealing with the same database, the proposed approach brings improvements in PD dysgraphia diagnosis and confirms the impact of fractional derivatives in kinematic analysis. Zobrazit méně

Speech articulation is produced by the movements of muscles in the larynx, pharynx, mouth and face. Therefore speech shows acoustic features as formants which are directly related with neuromotor actions of these muscles. The first two formants are strongly related with jaw and tongue muscular activity. Speech can be used as a simple and ubiquitous signal, easy to record and process, either locally or on e-Health platforms. This fact may open a wide set of applications in the study of… Zobrazit více

Speech articulation is produced by the movements of muscles in the larynx, pharynx, mouth and face. Therefore speech shows acoustic features as formants which are directly related with neuromotor actions of these muscles. The first two formants are strongly related with jaw and tongue muscular activity. Speech can be used as a simple and ubiquitous signal, easy to record and process, either locally or on e-Health platforms. This fact may open a wide set of applications in the study of functional grading and monitoring neurodegenerative diseases. A relevant question, in this sense, is how far speech correlates and neuromotor actions are related. This preliminary study is intended to find answers to this question by using surface electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. It is shown in the study that relevant correlations can be found among the surface electromyographic activity (dynamic muscle behavior) and the positions and first derivatives of the first formant (kinematic variables related to vertical velocity and acceleration of the joint jaw and tongue biomechanical system). As an application example, it is shown that the probability density function associated to these kinematic variables is more sensitive than classical features as Vowel Space Area (VSA) or Formant Centralization Ratio (FCR) in characterizing neuromotor degeneration in Parkinson’s Disease. Zobrazit méně

Neurodegenerative pathologies as Parkinson’s Disease (PD) show important distortions in speech, affecting fluency, prosody, articulation and phonation. Classically, measurements based on articulation gestures altering formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been proposed to measure speech distortion, but these markers are based mainly on static positions of sustained vowels. The present study introduces a measurement based on the mutual… Zobrazit více

Neurodegenerative pathologies as Parkinson’s Disease (PD) show important distortions in speech, affecting fluency, prosody, articulation and phonation. Classically, measurements based on articulation gestures altering formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been proposed to measure speech distortion, but these markers are based mainly on static positions of sustained vowels. The present study introduces a measurement based on the mutual information distance among probability density functions of kinematic correlates derived from formant dynamics. An absolute kinematic velocity associated to the position of the jaw and tongue articulation gestures is estimated and modeled statistically. The distribution of this feature may differentiate PD patients from normative speakers during sustained vowel emission. The study is based on a limited database of 53 male PD patients, contrasted to a very selected and stable set of eight normative speakers. In this sense, distances based on Kullback–Leibler divergence seem to be sensitive to PD articulation instability. Correlation studies show statistically relevant relationship between information contents based on articulation instability to certain motor and nonmotor clinical scores, such as freezing of gait, or sleep disorders. Remarkably, one of the statistically relevant correlations point out to the time interval passed since the first diagnostic. These results stress the need of defining scoring scales specifically designed for speech disability estimation and monitoring methodologies in degenerative diseases of neuromotor origin. Zobrazit méně

Parkinson's disease (PD) is the second most frequent neurodegenerative disorder. One typical hallmark of PD is disruption in execution of practised skills such as handwriting. This paper introduces a new methodology of kinematic features calculation based on fractional derivatives applied on PD handwriting. Discrimination power of basic kinematic features (velocity, acceleration, jerk) was evaluated by classification analysis (using support vector machines and random forests). For this purpose,… Zobrazit více

Parkinson's disease (PD) is the second most frequent neurodegenerative disorder. One typical hallmark of PD is disruption in execution of practised skills such as handwriting. This paper introduces a new methodology of kinematic features calculation based on fractional derivatives applied on PD handwriting. Discrimination power of basic kinematic features (velocity, acceleration, jerk) was evaluated by classification analysis (using support vector machines and random forests). For this purpose, 30 PD patients and 36 healthy controls were enrolled. In comparison with results reported in other works, the newly designed features based on fractional derivatives increased classification accuracy by 8 % in univariate analysis and by 10 % when employing the multivariate one. This study reveals an impact of fractional derivatives based features in analysis of Parkinsonic dysgraphia. Zobrazit méně

Hypokinetic dysarthria (HD) is a frequent symptom of idiopathic Parkinson's disease (PD). Although it is hypothesized its progress is tightly linked with changes in other motor/non-motor features of PD, it has not been proved yet. The aim of this work is to employ acoustic analysis of sustained phonation in order to identify significant correlates between phonatory measures and motor/non-motor deficits in a two-year follow-up study. For this purpose, we repeatedly quantified a sustained… Zobrazit více

Hypokinetic dysarthria (HD) is a frequent symptom of idiopathic Parkinson's disease (PD). Although it is hypothesized its progress is tightly linked with changes in other motor/non-motor features of PD, it has not been proved yet. The aim of this work is to employ acoustic analysis of sustained phonation in order to identify significant correlates between phonatory measures and motor/non-motor deficits in a two-year follow-up study. For this purpose, we repeatedly quantified a sustained vowel/a/ in 51 PD patients who were also assessed by 5 common clinical scales. In addition, a multivariate regression model was trained to predict the motor/non-motor deficits in the horizon of two years. Results suggest that mainly instability in vocal folds oscillation increases with the progress of PD and with overall cognitive decline. Based on the acoustic analysis, the change in clinical scores could be predicted with the error in the range of 11.83-19.60 %. Zobrazit méně

Hypokinetic dysarthria (HD) and freezing of gait (FOG) are both axial symptoms that occur in patients with Parkinson’s disease (PD). It is assumed they have some common pathophysiological mechanisms and therefore that speech disorders in PD can predict FOG deficits within the horizon of some years. The aim of this study is to employ a complex quantitative analysis of the phonation, articulation and prosody in PD patients in order to identify the relationship between HD and FOG, and establish a… Zobrazit více

Hypokinetic dysarthria (HD) and freezing of gait (FOG) are both axial symptoms that occur in patients with Parkinson’s disease (PD). It is assumed they have some common pathophysiological mechanisms and therefore that speech disorders in PD can predict FOG deficits within the horizon of some years. The aim of this study is to employ a complex quantitative analysis of the phonation, articulation and prosody in PD patients in order to identify the relationship between HD and FOG, and establish a mathematical model that would predict FOG deficits using acoustic analysis at baseline. We enrolled 75 PD patients who were assessed by 6 clinical scales including the Freezing of Gait Questionnaire (FOG–Q). We subsequently extracted 19 acoustic measures quantifying speech disorders in the fields of phonation, articulation and prosody. To identify the relationship between HD and FOG, we performed a partial correlation analysis. Finally, based on the selected acoustic measures, we trained regression models to predict the change in FOG during a 2-year follow-up. We identified significant correlations between FOG–Q scores and the acoustic measures based on formant frequencies (quantifying the movement of the tongue and jaw) and speech rate. Using the regression models, we were able to predict a change in particular FOG–Q scores with an error of between 7.4 and 17.0 %. This study is suggesting that FOG in patients with PD is mainly linked to improper articulation, a disturbed speech rate and to intelligibility. We have also proved that the acoustic analysis of HD at the baseline can be used as a predictor of the FOG deficit during 2 years of follow-up. This knowledge enables researchers to introduce new cognitive systems that predict gait difficulties in PD patients. Zobrazit méně

The prevalence of handwriting difficulties among school-aged children is around 10–30 %. Until now, there is no objective method to diagnose and rate developmental dysgraphia (DD) in Czech Republic. The goal of this study is to propose a new method of objective DD diagnosis based on quantitative analysis of online handwriting. For this purpose, we extracted a set of spatial, temporal, kinematic and dynamic features from three handwriting tasks. Consequently, we performed a correlation analysis… Zobrazit více

The prevalence of handwriting difficulties among school-aged children is around 10–30 %. Until now, there is no objective method to diagnose and rate developmental dysgraphia (DD) in Czech Republic. The goal of this study is to propose a new method of objective DD diagnosis based on quantitative analysis of online handwriting. For this purpose, we extracted a set of spatial, temporal, kinematic and dynamic features from three handwriting tasks. Consequently, we performed a correlation analysis between these features and score of handwriting proficiency screening questionnaire (HPSQ), in order to identify parameters with a good discrimination power. Using random forests classifier in combination with quantification of alphabet writing task, we reached nearly 80 % classification accuracy (77 % sensitivity, 83 % specificity). The classification accuracy was increased to 92 % (92 % sensitivity, 93 % specificity) by employing SFFS (Sequential Forward Feature Selection) method. This pilot study proves the possibility of automatic DD diagnosis in children cohort writing with cursive letters. Zobrazit méně

Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking, and even effective treatment of pathological voices. In search towards such a robust voice pathology detection system, we investigated three distinct classifiers within supervised learning and anomaly detection paradigms. We conducted a set of experiments using a variety of input data such as raw waveforms… Zobrazit více

Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking, and even effective treatment of pathological voices. In search towards such a robust voice pathology detection system, we investigated three distinct classifiers within supervised learning and anomaly detection paradigms. We conducted a set of experiments using a variety of input data such as raw waveforms, spectrograms, mel-frequency cepstral coefficients (MFCC), and conventional acoustic (dysphonic) features (AF). In comparison with previously published works, this article is the first to utilize combination of four different databases comprising normophonic and pathological recordings of sustained phonation of the vowel /a/ unrestricted to a subset of vocal pathologies. Furthermore, to our best knowledge, this article is the first to explore gradient-boosted trees and deep learning for this application. The following best classification performances measured by F1 score on dedicated test set were achieved: XGBoost (0.733) using AF and MFCC, DenseNet (0.621) using MFCC, and Isolation Forest (0.610) using AF. Even though these results are of exploratory character, conducted experiments do show promising potential of gradient boosting and deep learning methods to robustly detect voice pathologies. Zobrazit méně

OBJECTIVE:
Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades… Zobrazit více

OBJECTIVE:
Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies.

METHODS:
Among these methods, automatic analysis of speech -one of the first damaged skills in AD patients- is a natural and useful low cost tool for diagnosis.

RESULTS:
In this paper a non-linear multi-task approach based on automatic speech analysis is presented. Three tasks with different language complexity levels are analyzed, and promising results that encourage a deeper assessment are obtained. Automatic classification was carried out by using classic Multilayer Perceptron (MLP) and Deep Learning by means of Convolutional Neural Networks (CNN) (biologically- inspired variants of MLPs) over the tasks with classic linear features, perceptual features, Castiglioni fractal dimension and Multiscale Permutation Entropy.

CONCLUSION:
Finally, the most relevant features are selected by means of the non-parametric Mann- Whitney U-test. Zobrazit méně

Purpose: A videofluoroscopic analysis is the gold standard approach to determine whether a dysphagia problem exists. This procedure is invasive as it involves radiation but also provides the most direct physical evidence of swallowing problems. The main goal of this study was to evaluate an automatic tool based on voice analysis to support medical detection of dysphagia.

Methods: An automatic voice analysis system has been developed. Prior to (basal) and immediately following (viscosity)… Zobrazit více

Purpose: A videofluoroscopic analysis is the gold standard approach to determine whether a dysphagia problem exists. This procedure is invasive as it involves radiation but also provides the most direct physical evidence of swallowing problems. The main goal of this study was to evaluate an automatic tool based on voice analysis to support medical detection of dysphagia.

Methods: An automatic voice analysis system has been developed. Prior to (basal) and immediately following (viscosity) swallowing liquids of varying viscosity and volume, individuals with Parkinson Disease were required to produce the same test word. The acoustic features (linear and non-linear) of this word were then analyzed with regard to specific situations by standard and Machine Learning methods.

Results: The results indicated a high degree of accuracy in detecting voice associated with basal and viscosity states.

Conclusion: Thus, while the gold standard of dysphagia diagnosis continues to involve video-fluoroscopy analysis, the consideration of voice analysis may prove to be a far simpler and less invasive approach to diagnosis by advanced voice features. Zobrazit méně

Background and aims: Hypokinetic dysarthria (HD) is a common symptom of Parkinson’s disease (PD) which does not respond well to PD treatments. We investigated short-term effects of repetitive transcranial magnetic stimulation (rTMS) on HD in PD using acoustic analysis of speech. The main objective was to identify an optimal rTMS protocol and stimulation site to improve specific symptoms of HD in PD.Methods: We used 10 Hz and 1 Hz stimulation protocols and applied rTMS over the right posterior… Zobrazit více

Background and aims: Hypokinetic dysarthria (HD) is a common symptom of Parkinson’s disease (PD) which does not respond well to PD treatments. We investigated short-term effects of repetitive transcranial magnetic stimulation (rTMS) on HD in PD using acoustic analysis of speech. The main objective was to identify an optimal rTMS protocol and stimulation site to improve specific symptoms of HD in PD.Methods: We used 10 Hz and 1 Hz stimulation protocols and applied rTMS over the right posterior superior temporal gyrus (STG), the left orofacial primary motor area (OFM1), and over the vertex (V, a control stimulation site) in 10 PD patients with mild to moderate HD and 4 age-matched healthy controls. Stimulation sites and protocols were randomised across subjects and sessions. A cross-over design was used. Acoustic analysis of a sentence reading task performed inside the MR scanner was used to evaluate rTMS-induced effects on motor speech. The study is ongoing and neural (fMRI) correlates of stimulation-induced changes will also be assessed.Results: The preliminary results show particularly effects (p<0.05 in Wilcoxon test) of 1 Hz rTMS of the STG on total pause time, std of the 2nd formant, std of the fundamental voice frequency, and speech index of rhythmicity in PD, while some effects of 10 Hz rTMS of the OFM1 were also observed. Conclusion: Preliminary results demonstrate for the first time that low-frequency stimulation of the right STG may improve articulation, rhythmicity, intonation and pausing of speech in PD with HD. The study is ongoing. Zobrazit méně

Parkinson Disease (PD) is a neuromotor illness affecting general movements of different muscles, those implied in speech production being among them. The relevance of speech in monitoring illness progression has been documented in these last two decades. Most of the studies have concentrated in dysarthria and dysphonia induced by the syndrome. The present work is devoted to explore how PD affects the dynamic behavior of the speech neuromotor biomechanics (neuromechanics) involved in deficient… Zobrazit více

Parkinson Disease (PD) is a neuromotor illness affecting general movements of different muscles, those implied in speech production being among them. The relevance of speech in monitoring illness progression has been documented in these last two decades. Most of the studies have concentrated in dysarthria and dysphonia induced by the syndrome. The present work is devoted to explore how PD affects the dynamic behavior of the speech neuromotor biomechanics (neuromechanics) involved in deficient articulation (dysarthria), in contrast to classical measurements based on static features as extreme and central vowel triangle positions. A statistical distribution of the kinematic velocity of the lower jaw and tongue is introduced, which presents interesting properties regarding pattern recognition and classification. This function may be used to establish distances between different articulation profiles in terms of information theory. Results show that these distances are correlated with a set of tests currently used by neurologists in PD progress evaluation, and could be used in elaborating new speech testing protocols. Zobrazit méně

Online handwritten signals analysis for biomedical applications has received lesser attention from the international scientific community than other biometric signals such as electroencephalogram (EEG), electrocardiogram (ECG), magnetic resonance imaging signals (MRI), speech, etc. However, handwritten signals are useful for biometric security applications, especially in the case of signature, but to support pathology diagnose/monitoring as well. Obviously, while utilising handwriting in one… Zobrazit více

Online handwritten signals analysis for biomedical applications has received lesser attention from the international scientific community than other biometric signals such as electroencephalogram (EEG), electrocardiogram (ECG), magnetic resonance imaging signals (MRI), speech, etc. However, handwritten signals are useful for biometric security applications, especially in the case of signature, but to support pathology diagnose/monitoring as well. Obviously, while utilising handwriting in one field, there are implications in the other one and privacy concerns can arise. A good example is a biometric security system that stores the whole biometric template. It is desirable to reduce the template to the relevant information required for security, removing those characteristics that can permit the identification of pathologies. In this paper, we summarize the main aspects of handwritten signals with special emphasis on medical applications (Alzheimer's disease, Parkinson's disease, mild cognitive impairment, essential tremor, depression, dysgraphia, etc.) and security. In addition, it is important to remark that health and security issues cannot be easily isolated, and an application in one field should take care of the other. Zobrazit méně

The research described is intended to give a description of articulation dynamics as a correlate of the kinematic behavior of the jaw-tongue biomechanical system, encoded as a probability distribution of an absolute joint velocity. This distribution may be used in detecting and grading speech from patients affected by neurodegenerative illnesses, as Parkinson Disease.

Hypokinetic dysarthria (HD) and freezing of gait (FOG) are frequent symptoms of Parkinson's disease (PD). The aim of this work is to reveal pathological mechanisms common for HD and FOG, and use acoustic analysis of dysarthric speech to assess the gait difficulties in PD. We used a correlation analysis to investigate a relationship between speech features and FOG evaluated by freezing of gait questionnaire (FOG-Q). We found speech features quantifying reduced mobility of the articulatory organs… Zobrazit více

Hypokinetic dysarthria (HD) and freezing of gait (FOG) are frequent symptoms of Parkinson's disease (PD). The aim of this work is to reveal pathological mechanisms common for HD and FOG, and use acoustic analysis of dysarthric speech to assess the gait difficulties in PD. We used a correlation analysis to investigate a relationship between speech features and FOG evaluated by freezing of gait questionnaire (FOG-Q). We found speech features quantifying reduced mobility of the articulatory organs significantly correlated with all parts of the questionnaire. Next, we built multivariate regression models to estimate the FOG-Q total score. With this approach, mean estimation error rate of 14.71% was achieved. We confirmed the previous findings of a close relationship between HD and FOG in PD. Furthermore, we showed it is possible to accurately (with the error of approximately 0.5 points) estimate FOG-Q using a reasonable number of conventional speech features. Zobrazit méně

Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and… Zobrazit více

Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and clinical status of the speakers was observed. In case of univariate classification analysis, sensitivity of 62.63 % (imprecise articulation), 61.62% (dysprosody), 71.72% (speech dysfluency) and 59.60% (speech quality deterioration) was achieved. Multivariate classification analysis improved the classification performance. Sensitivity of 83.42% using only two features describing imprecise articulation and speech quality deterioration in HD was achieved. We showed the promising potential of the selected speech features and especially the use of poem recitation task to quantify and identify HD in PD. Zobrazit méně

Existing literature about online handwriting analysis to support pathology diagnosis has taken advantage of in-air trajectories. A similar situation occurred in biometric security applications where the goal is to identify or verify an individual using his signature or handwriting. These studies do not consider the distance of the pen tip to the writing surface. This is due to the fact that current acquisition devices do not provide height formation. However, it is quite straightforward to… Zobrazit více

Existing literature about online handwriting analysis to support pathology diagnosis has taken advantage of in-air trajectories. A similar situation occurred in biometric security applications where the goal is to identify or verify an individual using his signature or handwriting. These studies do not consider the distance of the pen tip to the writing surface. This is due to the fact that current acquisition devices do not provide height formation. However, it is quite straightforward to differentiate movements at two different heights (a) short distance: height lower or equal to 1 cm above a surface of digitizer, the digitizer provides x and y coordinates; (b) long distance: height exceeding 1 cm, the only information available is a time stamp that indicates the time that a specific stroke has spent at long distance. Although short distance has been used in several papers, long distances have been ignored and will be investigated in this paper. In this paper, we will analyze a large set of databases (BIOSECUR-ID, EMOTHAW, PaHaW, OXYGEN-THERAPY, and SALT), which contain a total amount of 663 users and 17,951 files. We have specifically studied (a) the percentage of time spent on-surface, in-air at short distance, and in-air at long distance for different user profiles (pathological and healthy users) and different tasks; (b) the potential use of these signals to improve classification rates. Our experimental results reveal that long distance movements represent a very small portion of the total execution time (0.5% in the case of signatures and 10.4% for uppercase words of BIOSECUR-ID, which is the largest database). In addition, significant differences have been found in the comparison of pathological versus control group for letter “l” in PaHaW database (p = 0.0157) and crossed pentagons in SALT database (p = 0.0122). Zobrazit méně

This paper describes a preliminary investigation of Voice Pathology Detection using Deep Neural Networks (DNN). We used voice recordings of sustained vowel /a/ produced at normal pitch from German corpus Saarbruecken Voice Database (SVD). This corpus contains voice recordings and electroglottograph signals of more than 2 000 speakers. The idea behind this experiment is the use of convolutional layers in combination with recurrent Long-Short-Term-Memory (LSTM) layers on raw audio signal. Each… Zobrazit více

This paper describes a preliminary investigation of Voice Pathology Detection using Deep Neural Networks (DNN). We used voice recordings of sustained vowel /a/ produced at normal pitch from German corpus Saarbruecken Voice Database (SVD). This corpus contains voice recordings and electroglottograph signals of more than 2 000 speakers. The idea behind this experiment is the use of convolutional layers in combination with recurrent Long-Short-Term-Memory (LSTM) layers on raw audio signal. Each recording was split into 64 ms Hamming windowed segments with 30 ms overlap. Our trained model achieved 71.36% accuracy with 65.04% sensitivity and 77.67% specificity on 206 validation files and 68.08% accuracy with 66.75% sensitivity and 77.89% specificity on 874 testing files. This is a promising result in favor of this approach because it is comparable to similar previously published experiment that used different methodology. Further investigation is needed to achieve the state-of-the-art results. Zobrazit méně

Neurodegenerative pathologies produce important distortions in speech. Parkinson’s Disease (PD) leaves marks in fluency, prosody, articulation and phonation. Certain measurements based in configurations of the articulation organs inferred from formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been classically used in this sense, but these markers represent mainly the static positions of sustained vowels on the vowel triangle. The present study… Zobrazit více

Neurodegenerative pathologies produce important distortions in speech. Parkinson’s Disease (PD) leaves marks in fluency, prosody, articulation and phonation. Certain measurements based in configurations of the articulation organs inferred from formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been classically used in this sense, but these markers represent mainly the static positions of sustained vowels on the vowel triangle. The present study proposes a measurement based on the mutual information contents of kinematic correlates derived from formant dynamics. An absolute kinematic velocity associated to the position of the articulation organs, involving the jaw and tongue is estimated and modelled statistically. The distribution of this feature is rather different in PD patients than in normative speakers when sustained vowels are considered. Therefore, articulation failures may be detected even in single sustained vowels. The study has processed a limited database of 40 female and 54 male PD patients, contrasted to a very selected and stable set of normative speakers. Distances based on Kullback-Leibler’s Divergence have shown to be sensitive to PD articulation instability. Correlation measurements show that the distance proposed shows statistically relevant relationship with certain motor and non-motor behavioral observations, as freezing of gait, or sleep disorders. These results point out to the need of defining scoring scales specifically designed for speech-based diagnose and monitoring methodologies in degenerative diseases of neuromotor origin. Zobrazit méně

Hypokinetic dysarthria (HD) occurs in 90% of Parkinson’s disease (PD) patients. It manifests specifically in the areas of articulation, phonation, prosody, speech fluency, and faciokinesis. We aimed to systematically review papers on HD in PD with a special focus on (1) early PD diagnosis and monitoring of the disease progression using acoustic voice and speech analysis, and (2) functional imaging studies exploring neural correlates of HD in PD, and (3) clinical studies using acoustic analysis… Zobrazit více

Hypokinetic dysarthria (HD) occurs in 90% of Parkinson’s disease (PD) patients. It manifests specifically in the areas of articulation, phonation, prosody, speech fluency, and faciokinesis. We aimed to systematically review papers on HD in PD with a special focus on (1) early PD diagnosis and monitoring of the disease progression using acoustic voice and speech analysis, and (2) functional imaging studies exploring neural correlates of HD in PD, and (3) clinical studies using acoustic analysis to evaluate effects of dopaminergic medication and brain stimulation. A systematic literature search of articles written in English before March 2016 was conducted in the Web of Science, PubMed, SpringerLink, and IEEE Xplore databases using and combining specific relevant keywords. Articles were categorized into three groups: (1) articles focused on neural correlates of HD in PD using functional imaging (n = 13); (2) articles dealing with the acoustic analysis of HD in PD (n = 52); and (3) articles concerning specifically dopaminergic and brain stimulation-related effects as assessed by acoustic analysis (n = 31); the groups were then reviewed. We identified 14 combinations of speech tasks and acoustic features that can be recommended for use in describing the main features of HD in PD. While only a few acoustic parameters correlate with limb motor symptoms and can be partially relieved by dopaminergic medication, HD in PD seems to be mainly related to non-dopaminergic deficits and associated particularly with non-motor symptoms. Future studies should combine non-invasive brain stimulation with voice behavior approaches to achieve the best treatment effects by enhancing auditory-motor integration. Zobrazit méně

Developmental dysgraphia, being observed among 10–30% of school-aged children, is a disturbance or difficulty in the production of written language that has to do with the mechanics of writing. The objective of this study is to propose a method that can be used for automated diagnosis of this disorder, as well as for estimation of difficulty level as determined by the handwriting proficiency screening questionnaire. We used a digitizing tablet to acquire handwriting and consequently employed a… Zobrazit více

Developmental dysgraphia, being observed among 10–30% of school-aged children, is a disturbance or difficulty in the production of written language that has to do with the mechanics of writing. The objective of this study is to propose a method that can be used for automated diagnosis of this disorder, as well as for estimation of difficulty level as determined by the handwriting proficiency screening questionnaire. We used a digitizing tablet to acquire handwriting and consequently employed a complex parameterization in order to quantify its kinematic aspects and hidden complexities. We also introduced a simple intrawriter normalization that increased dysgraphia discrimination and HPSQ estimation accuracies. Using a random forest classifier, we reached 96% sensitivity and specificity, while in the case of automated rating by the HPSQ total score, we reached 10% estimation error. This study proves that digital parameterization of pressure and altitude/tilt patterns in children with dysgraphia can be used for preliminary diagnosis of this writing disorder. Zobrazit méně

This paper deals with Parkinson's disease (PD) severity estimation according to the Unified Parkinson's Disease Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS in ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole… Zobrazit více

This paper deals with Parkinson's disease (PD) severity estimation according to the Unified Parkinson's Disease Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS in ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole corpus and these groups separately using a wide range of conventional and novel speech features. We used guided regularized random forest algorithm to select features with maximum clinical information and performed random forests regression to estimate PD severity. According to significant correlations between true UPDRS in scores and scores predicted by the proposed methodology it was shown that information extracted through variety of speech tasks can be used to estimate the degree of PD severity. Zobrazit méně

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1–1 %, and an annual incidence between 1.3–2.0/10,000 inhabitants. The most obvious symptoms are movement-related such as tremor, rigidity, slowness of movement and walking difficulties and frequently these are the symptoms that lead to the PD diagnoses but also they could have In this sense voice analysis is a safe, non-invasive, and reliable screening procedure… Zobrazit více

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1–1 %, and an annual incidence between 1.3–2.0/10,000 inhabitants. The most obvious symptoms are movement-related such as tremor, rigidity, slowness of movement and walking difficulties and frequently these are the symptoms that lead to the PD diagnoses but also they could have In this sense voice analysis is a safe, non-invasive, and reliable screening procedure for PD patients with dysphagia, which could detect patients at high risk of clinically significant aspiration. In this paper we will present a part of an ongoing project that will evaluate automatic speech analysis based on linear and non-linear features. These can be reliable predictors/indicators of swallowing and balance impairments in PD. An important advantage of voice analysis is its low intrusiveness and easy implementation in clinical practice. Thus, if a significant correlation between these simple analyses and the gold standard video-fluoroscopic analysis will imply simpler and less stressing diagnostic test for the patients as well as the use of cheaper analysis systems. Zobrazit méně

Background
Impairment of speech prosody is characteristic for Parkinson's disease (PD) and does not respond well to dopaminergic treatment.

Objectives
We assessed whether baseline acoustic parameters, alone or in combination with other predominantly non-dopaminergic symptoms may predict global cognitive decline as measured by the Addenbrooke's cognitive examination (ACE-R) and/or worsening of cognitive status as assessed by a detailed neuropsychological… Zobrazit více

Background
Impairment of speech prosody is characteristic for Parkinson's disease (PD) and does not respond well to dopaminergic treatment.

Objectives
We assessed whether baseline acoustic parameters, alone or in combination with other predominantly non-dopaminergic symptoms may predict global cognitive decline as measured by the Addenbrooke's cognitive examination (ACE-R) and/or worsening of cognitive status as assessed by a detailed neuropsychological examination.

Methods
Forty-four consecutive non-depressed PD patients underwent clinical and cognitive testing, and acoustic voice analysis at baseline and at the two-year follow-up. Influence of speech and other clinical parameters on worsening of the ACE-R and of the cognitive status was analyzed using linear and logistic regression.

Results
The cognitive status (classified as normal cognition, mild cognitive impairment and dementia) deteriorated in 25% of patients during the follow-up. The multivariate linear regression model consisted of the variation in range of the fundamental voice frequency (F0VR) and the REM Sleep Behavioral Disorder Screening Questionnaire (RBDSQ). These parameters explained 37.2% of the variability of the change in ACE-R. The most significant predictors in the univariate logistic regression were the speech index of rhythmicity (SPIR; p = 0.012), disease duration (p = 0.019), and the RBDSQ (p = 0.032). The multivariate regression analysis revealed that SPIR alone led to 73.2% accuracy in predicting a change in cognitive status. Combining SPIR with RBDSQ improved the prediction accuracy of SPIR alone by 7.3%.

Conclusions
Impairment of speech prosody together with symptoms of RBD predicted rapid cognitive decline and worsening of PD cognitive status during a two-year period. Zobrazit méně

Hypokinetic dysarthria (HD) is a frequent speech disorder associated with idiopathic Parkinson's disease (PD). It affects all dimensions of speech production. One of the most common features of HD is dysprosody that is characterized by alterations of rhythm and speech rate, flat speech melody, and impairment of speech intensity control. Dysprosody has a detrimental impact on speech naturalness and intelligibility.

This paper deals with quantitative prosodic analysis of neutral… Zobrazit více

Hypokinetic dysarthria (HD) is a frequent speech disorder associated with idiopathic Parkinson's disease (PD). It affects all dimensions of speech production. One of the most common features of HD is dysprosody that is characterized by alterations of rhythm and speech rate, flat speech melody, and impairment of speech intensity control. Dysprosody has a detrimental impact on speech naturalness and intelligibility.

This paper deals with quantitative prosodic analysis of neutral, stress-modified and rhymed speech in patients with PD. The analysis of prosody is based on quantification of monopitch, monoloudness, and speech rate abnormalities. Experimental dataset consists of 98 patients with PD and 51 healthy speakers. For the purpose of HD identification, sequential floating feature selection algorithm and random forests classifier is used. In this paper, we also introduce a concept of permutation test applied in the field of acoustic analysis of dysarthric speech.

Prosodic features obtained from stress-modified reading task provided higher classification accuracies compared to the ones extracted from reading task with neutral emotion demonstrating the importance of stress in speech prosody. Features calculated from poem recitation task outperformed both reading tasks in the case of gender-undifferentiated analysis showing that rhythmical demands can in general lead to more precise identification of HD. Additionally, some gender-related patterns of dysprosody has been observed.

This paper confirms reduced variation of fundamental frequency in PD patients with HD. Interestingly, increased variability of speech intensity compared to healthy speakers has been detected. Regarding speech rate disturbances, our results does not report any particular pattern. We conclude further development of prosodic features quantifying the relationship between monopitch, monoloudness and speech rate disruptions in HD can have a great potential in future PD analysis. Zobrazit méně

Objective:
We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD.

Methods and material:
The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean… Zobrazit více

Objective:
We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD.

Methods and material:
The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM).

Results:
For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features.

Conclusion:
Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls. Zobrazit méně

Up to 90 % of patients with Parkinson’s disease (PD) suffer from hypokinetic dysathria (HD) which is also manifested in the field of phonation. Clinical signs of HD like monoloudness, monopitch or hoarse voice are usually quantified by conventional clinical interpretable features (jitter, shimmer, harmonic-to-noise ratio, etc.). This paper provides large and robust insight into perceptual analysis of 5 Czech vowels of 84 PD patients and proves that despite the clinical inexplicability the… Zobrazit více

Up to 90 % of patients with Parkinson’s disease (PD) suffer from hypokinetic dysathria (HD) which is also manifested in the field of phonation. Clinical signs of HD like monoloudness, monopitch or hoarse voice are usually quantified by conventional clinical interpretable features (jitter, shimmer, harmonic-to-noise ratio, etc.). This paper provides large and robust insight into perceptual analysis of 5 Czech vowels of 84 PD patients and proves that despite the clinical inexplicability the perceptual features outperform the conventional ones, especially in terms of discrimination power (classification accuracy ACC=92 %, sensitivity SEN=93 %, specificity SPE=92 %) and partial correlation with clinical scores like UPDRS (Unified Parkinson’s disease rating scale), MMSE (Mini-mental state examination) or FOG (Freezing of gait questionnaire), where p<0.0001.
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Background

Impaired speech prosody is common in Parkinson's disease (PD). We assessed the impact of PD and levodopa on MRI resting-state functional connectivity (rs-FC) underlying speech prosody control.

Methods

We studied 19 PD patients in the OFF and ON dopaminergic conditions and 15 age-matched healthy controls using functional MRI and seed partial least squares correlation (PLSC) analysis. In the PD group, we also correlated levodopa-induced rs-FC changes with the… Zobrazit více

Background

Impaired speech prosody is common in Parkinson's disease (PD). We assessed the impact of PD and levodopa on MRI resting-state functional connectivity (rs-FC) underlying speech prosody control.

Methods

We studied 19 PD patients in the OFF and ON dopaminergic conditions and 15 age-matched healthy controls using functional MRI and seed partial least squares correlation (PLSC) analysis. In the PD group, we also correlated levodopa-induced rs-FC changes with the results of acoustic analysis.

Results

The PLCS analysis revealed a significant impact of PD but not of medication on the rs-FC strength of spatial correlation maps seeded by the anterior cingulate (p = 0.006), the right orofacial primary sensorimotor cortex (OF_SM1; p = 0.025) and the right caudate head (CN; p = 0.047). In the PD group, levodopa-induced changes in the CN and OF_SM1 connectivity strengths were related to changes in speech prosody.

Conclusions

We demonstrated an impact of PD but not of levodopa on rs-FC within the brain networks related to speech prosody control. When only the PD patients were taken into account, the association between treatment-induced changes in speech prosody and changes in rs-FC within the associative striato-prefrontal and motor speech networks was found. Zobrazit méně

This paper presents a study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a special focus on parametrization techniques. It provides a description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new… Zobrazit více

This paper presents a study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a special focus on parametrization techniques. It provides a description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition. The significance of these features was tested on 3 (English, Spanish and Czech) pathological voice databases with respect to classification accuracy, sensitivity and specificity. To our best knowledge the introduced approach based on complex feature extraction and robust testing outperformed all works that have been published already in this field. The results (accuracy, sensitivity and specificity equal to 100.0±0.0%) are discussable in the case of Massachusetts Eye and Ear Infirmary (MEEI) database because of its limitation related to a length of sustained vowels, however in the case of Príncipe de Asturias (PdA) Hospital in Alcalá de Henares of Madrid database we made improvements in classification accuracy (82.1±3.3%) and specificity (83.8±5.1%) when considering a single-classifier approach. Hopefully, large improvements may be achieved in the case of Czech Parkinsonian Speech Database (PARCZ), which are discussed in this work as well. All the features introduced in this work were identified by Mann–Whitney U test as significant (p<0.05) when processing at least one of the mentioned databases. The paper also mentions some ideas for the future work in the field of pathological speech signal analysis that can be valuable especially under the clinical point of view. Zobrazit méně

This paper deals with an acoustic analysis of hypokinetic dysarthria in patients with Parkinson's disease (PD). The analysis is based on parametrization of five basic Czech vowels using conventional features and parameters based on empirical mode decomposition (EMD). Experimental dataset consists of 84 PD patients with different disease progress and 49 healthy controls. From the single-vowel-analysis point of view we observed that sustained vowels pronounced with minimum intensity (not… Zobrazit více

This paper deals with an acoustic analysis of hypokinetic dysarthria in patients with Parkinson's disease (PD). The analysis is based on parametrization of five basic Czech vowels using conventional features and parameters based on empirical mode decomposition (EMD). Experimental dataset consists of 84 PD patients with different disease progress and 49 healthy controls. From the single-vowel-analysis point of view we observed that sustained vowels pronounced with minimum intensity (not whispering) outperformed the other vowels'​ realization (including the most popular sustained vowel [a] pronounced with normal intensity). Then we employed a classification along with feature selection and again obtained the best results in the case of silent sustained vowels (accuracy ACC = 84 %, sensitivity SEN = 86% and specificity SPE = 82 %). Finally we considered classification of PD using different vowels' realization and reached accuracy = 94 %, sensitivity = 96% and specificity = 90 %. Features based on EMD significantly improved the results. Zobrazit méně

This paper deals with a complex acoustic analysis of phonation in patients with Parkinson's disease (PD) with a special focus on estimation of disease progress that is described by 7 different clinical scales (e. g. Unified Parkinson's disease rating scale or Beck depression inventory). The analysis is based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using classification and regression trees we estimated all clinical scores with maximal error lower or equal to 13 %. Best… Zobrazit více

This paper deals with a complex acoustic analysis of phonation in patients with Parkinson's disease (PD) with a special focus on estimation of disease progress that is described by 7 different clinical scales (e. g. Unified Parkinson's disease rating scale or Beck depression inventory). The analysis is based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using classification and regression trees we estimated all clinical scores with maximal error lower or equal to 13 %. Best estimation was observed in the case of Mini-mental state examination (MAE = 0.77, estimation error 5.50 %). Finally, we proposed a binary classification based on random forests that is able to identify Parkinson's disease with sensitivity SEN = 92.86% (SPE = 85.71 %). The parametrization process was based on extraction of 107 speech features quantifying different clinical signs of hypokinetic dysarthria present in PD. Zobrazit méně

In this paper, we evaluate the contribution of different handwriting modalities to the diagnosis of Parkinson's disease. We analyse on-surface movement, in-air movement and pressure exerted on the tablet surface. Especially in-air movement and pressure-based features have been rarely taken into account in previous studies. We show that pressure and in-air movement also possess information that is relevant for the diagnosis of Parkinson's Disease (PD) from handwriting. In addition to the… Zobrazit více

In this paper, we evaluate the contribution of different handwriting modalities to the diagnosis of Parkinson's disease. We analyse on-surface movement, in-air movement and pressure exerted on the tablet surface. Especially in-air movement and pressure-based features have been rarely taken into account in previous studies. We show that pressure and in-air movement also possess information that is relevant for the diagnosis of Parkinson's Disease (PD) from handwriting. In addition to the conventional kinematic and spatio-temporal features, we present a group of the novel features based on entropy and empirical mode decomposition of the handwriting signal. The presented results indicate that handwriting can be used as biomarker for PD providing classification performance around 89% area under the ROC curve (AUC) for PD classification. Zobrazit méně

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1-1 %, and an annual incidence between 1.3-2.0/10000 inhabitants. The mean age at diagnosis of PD is 55 and most patients are between 50 and 80 years old. The most obvious symptoms are movement-related; these include tremor, rigidity, slowness of movement and walking difficulties. Frequently these are the symptoms that lead to the PD diagnoses. Later, thinking and… Zobrazit více

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1-1 %, and an annual incidence between 1.3-2.0/10000 inhabitants. The mean age at diagnosis of PD is 55 and most patients are between 50 and 80 years old. The most obvious symptoms are movement-related; these include tremor, rigidity, slowness of movement and walking difficulties. Frequently these are the symptoms that lead to the PD diagnoses. Later, thinking and behavioral problems may arise, and other symptoms include cognitive impairment and sensory, sleep and emotional problems. In this paper we will present an ongoing project that will evaluate if voice and handwriting analysis can be reliable predictors/indicators of swallowing and balance impairments in PD. An important advantage of voice and handwritten analysis is its low intrusiveness and easy implementation in clinical practice. Thus, if a significant correlation between these simple analyses and the gold standard video-fluoroscopic analysis will imply simpler and less stressing diagnostic test for the patients as well as the use of cheaper analysis systems. Zobrazit méně

Parkinson’s disease (PD) is a neurodegenerative disorder which impairs motor skills, speech, and other functions such as behavior, mood, and cognitive processes. One of the most typical clinical hallmarks of PD is handwriting deterioration, usually the first manifestation of PD. The aim of this study is twofold: (a) to find a subset of handwriting features suitable for identifying subjects with PD and (b) to build a predictive model to efficiently diagnose PD. We collected handwriting samples… Zobrazit více

Parkinson’s disease (PD) is a neurodegenerative disorder which impairs motor skills, speech, and other functions such as behavior, mood, and cognitive processes. One of the most typical clinical hallmarks of PD is handwriting deterioration, usually the first manifestation of PD. The aim of this study is twofold: (a) to find a subset of handwriting features suitable for identifying subjects with PD and (b) to build a predictive model to efficiently diagnose PD. We collected handwriting samples from 37 medicated PD patients and 38 age- and sex- matched controls. The handwriting samples were collected during seven tasks such as writing a syllable, word, or sentence. Every sample was used to extract the handwriting measures. In addition to conventional kinematic and spatio-temporal handwriting measures, we also computed novel handwriting measures based on entropy, signal energy, and empirical mode decomposition of the handwriting signals. The selected features were fed to the support vector machine classifier with radial Gaussian kernel for automated diagnosis. The accuracy of the classification of PD was as high as 88:13%, with the highest values of sensitivity and specificity equal to 89.47% and 91.89%, respectively. Handwriting may be a valuable marker as a diagnostic and screening tool. Zobrazit méně

Background and objective
Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which… Zobrazit více

Background and objective
Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis.

Methods
We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls.

Results
By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy.

Conclusions
Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD. Zobrazit méně

Aim:
The aim of this study was to assess micrographia in patients with Parkinson’s disease (PD) as compared to healthy controls (HC) using a digitizing tablet.
Methods:
We included 40 PD (mean 68.6 ± 11.36 years, duration of illness 8.02 ± 4.79 years) and 40 age- and sex-matched HC (mean 62.55 ± 11. 22 years). All subjects were right-handed, without the presence of depression or dementia. Each subject underwent seven exercises for writing and drawing of Archimedes spiral and ellipses… Zobrazit více

Aim:
The aim of this study was to assess micrographia in patients with Parkinson’s disease (PD) as compared to healthy controls (HC) using a digitizing tablet.
Methods:
We included 40 PD (mean 68.6 ± 11.36 years, duration of illness 8.02 ± 4.79 years) and 40 age- and sex-matched HC (mean 62.55 ± 11. 22 years). All subjects were right-handed, without the presence of depression or dementia. Each subject underwent seven exercises for writing and drawing of Archimedes spiral and ellipses using a digitizing tablet. The speed parameters of micrographia and drawing during the movement of a pen in the air and on the tablet were evaluated. The Mann-Whitney U test, Spearman correlation and Benjamini-Hochbergs method were used for statistical data analysis.
Results:
A statistically significant reduction in parameters of velocity, acceleration, and jerk was found when comparing both groups during writing. Changes were more pronounced with increased length of the written segment. The differences between the two groups were more pronounced when the in-air movements were assessed, i.e. during movement preparation. The values decreased up to 20% compared to HC.
Conclusion:
PD-specific changes assessed with a digitizing tablet were demonstrated not only during writing but also during preparation for writing. The results of the study may have a direct clinical impact: further research into its use as a clinical marker of early PD is likely to follow. Zobrazit méně

One of the most frequent clinical hallmarks of Parkinson's disease (PD) is micrographia. Micrographia in PD is characterized by the decreased letter size and by changes in the kinematic aspects including increased movement time, decreased velocities and accelerations, and increased number of changes in velocity and acceleration. Based on the literature survey we proposed template to acquire handwriting during different tasks. In addition to well established tasks for PD diagnosis such as… Zobrazit více

One of the most frequent clinical hallmarks of Parkinson's disease (PD) is micrographia. Micrographia in PD is characterized by the decreased letter size and by changes in the kinematic aspects including increased movement time, decreased velocities and accelerations, and increased number of changes in velocity and acceleration. Based on the literature survey we proposed template to acquire handwriting during different tasks. In addition to well established tasks for PD diagnosis such as Archimedean spiral, we designed new tasks to acquire all aspects of micrographia. The database consists of eight different handwriting samples from seventy-five subjects. The presented results shows almost 80% overall classification accuracy. Zobrazit méně

Parkinsons disease (PD) is neurodegenerative disorder with very high prevalence rate occurring mainly among elderly. One of the most typical symptoms of PD is deterioration of handwriting that is usually the first manifestation of Parkinsons disease. In this study, a new modality - in-air trajectory during handwriting - is proposed to efficiently diagnose PD. Experimental results showed that analysis of in-air trajectories is capable of assessing subtle motor abnormalities that are connected… Zobrazit více

Parkinsons disease (PD) is neurodegenerative disorder with very high prevalence rate occurring mainly among elderly. One of the most typical symptoms of PD is deterioration of handwriting that is usually the first manifestation of Parkinsons disease. In this study, a new modality - in-air trajectory during handwriting - is proposed to efficiently diagnose PD. Experimental results showed that analysis of in-air trajectories is capable of assessing subtle motor abnormalities that are connected with PD. Moreover, conjunction of in-air trajectories with conventional on-surface handwriting allows us to build predictive model with PD classification accuracy over 80%. In total, we compute over 600 handwriting features. Then, we select smaller subset of these features using two feature selection algorithms: Mann-Whitney U-test filter and relief algorithm, and map these feature subsets to binary classification response using support vector machines. Zobrazit méně

In this paper, we present a new hand database called Tecnocampus Hand Image Database that includes right hand, palm and dorsal images. All the images have been acquired with three different sensors (visible, near-infrared and thermal). This database consists of 100 people acquired in five different acquisition sessions, two images per session and palm/dorsal sides. The total amount of pictures is 6.000, and it is mainly developed for hand image biometric recognition purposes. In addition, the… Zobrazit více

In this paper, we present a new hand database called Tecnocampus Hand Image Database that includes right hand, palm and dorsal images. All the images have been acquired with three different sensors (visible, near-infrared and thermal). This database consists of 100 people acquired in five different acquisition sessions, two images per session and palm/dorsal sides. The total amount of pictures is 6.000, and it is mainly developed for hand image biometric recognition purposes. In addition, the database has been studied from the information theory point of view, and we found that this highest level of information is achieved in thermal spectrum. Furthermore, a low level of mutual information between different spectrums is also demonstrated. This opens an interesting research field in multi-sensor data fusion. Zobrazit méně

En este artículo describimos una nueva bases de datos facial multiespectral adquirida en nuestro grupo. Las principales características son: 4 sesiones de adquisición, 41 usuarios, diferentes condiciones de iluminación y adquisición visible, térmica e infraroja cercana. Está disponible de forma gratuita para los grupos de investigación que lo soliciten.

The paper deals with the methods of non-invasive analysis of neurological disorders, focusing on speech signal processing and processing of handwritten text. The paper describes the whole procedure of the automated analysis of the disorder while the greatest attention is paid to a parameterization. In the case of speech signal analysis, the state-of-the-art features evaluating a presence of hoarseness, breathiness and hypernasality are mentioned. Nonlinear dynamic parameters and parameters… Zobrazit více

The paper deals with the methods of non-invasive analysis of neurological disorders, focusing on speech signal processing and processing of handwritten text. The paper describes the whole procedure of the automated analysis of the disorder while the greatest attention is paid to a parameterization. In the case of speech signal analysis, the state-of-the-art features evaluating a presence of hoarseness, breathiness and hypernasality are mentioned. Nonlinear dynamic parameters and parameters derived from the empirical mode decomposition (EMD) are compared. Based on the tests, from the point of description of a noise component of signal, the best results were obtained using the approximation entropy, the largest Lyapunov exponent and parameters based on Teager-Kaiser energy operator, which is calculated from the first intrinsic mode function (IMF). In the case of handwritten text analysis, the most used exercises describing a tremor and movement dynamics are mentioned. The new approaches of hand movement analysis at a time when the pen tip does not touch the paper have been also proposed. Finally the paper discusses different applications of speech signal and handwriting text parameterization. Zobrazit méně

We have seen how to avoid the cold finger areas in order to get a better segmented TH image. These approaches are only necessary when temperatures from the finger are close to the surface. Once the TH image is well segmented we have observed different performance.

Publikace se věnuje diagnostice nejčastějších druhů narušené komunikační schopnosti, se kterými se profesionálové, především z řad logopedů, setkávají u dospělých. V úvodních kapitolách je vymezen fenomén narušená komunikační schopnost a specifika jejího diagnostikování. Každý druh narušené komunikační schopnosti je nejprve prezentován v jeho základní charakteristice (vymezení, incidence, prevalence, etiologie, klasifikace); navazují specifika diagnostikování, popis konkrétních diagnostických… Zobrazit více

Publikace se věnuje diagnostice nejčastějších druhů narušené komunikační schopnosti, se kterými se profesionálové, především z řad logopedů, setkávají u dospělých. V úvodních kapitolách je vymezen fenomén narušená komunikační schopnost a specifika jejího diagnostikování. Každý druh narušené komunikační schopnosti je nejprve prezentován v jeho základní charakteristice (vymezení, incidence, prevalence, etiologie, klasifikace); navazují specifika diagnostikování, popis konkrétních diagnostických metod a technik používaných u nás a v zahraničí a nakonec doporučení pro praxi s přehledem nejdůležitější související odborné literatury. Kniha je určena pro logopedy, neurology, psychiatry, otorinolaryngology, psychology a odborníky z dalších pomáhajících profesí. Zobrazit méně

A novel multi-spectral focus measure that is based on algorithms for interest point detection, particularly on the FAST (Features from Accelerated Segment Test), Fast Hessian and Harris-Laplace detector, is described in this paper. The proposed measure methods are compared with commonly used focus measure techniques like energy of image gradient, sum-modified Laplacian, Tenenbaum's algorithm or spatial frequency when testing their reliability and performance. The measures have been tested on a… Zobrazit více

A novel multi-spectral focus measure that is based on algorithms for interest point detection, particularly on the FAST (Features from Accelerated Segment Test), Fast Hessian and Harris-Laplace detector, is described in this paper. The proposed measure methods are compared with commonly used focus measure techniques like energy of image gradient, sum-modified Laplacian, Tenenbaum's algorithm or spatial frequency when testing their reliability and performance. The measures have been tested on a newly created database containing 420 images acquired in visible, near-infrared and thermal spectrum (7 objects in each spectrum). Algorithms based on the interest point detectors proved to be good focus measures satisfying all the requirements described in the paper, especially in thermal spectrum. It is shown that these algorithms outperformed all commonly used methods in thermal spectrum and therefore can serve as a new and more accurate focus measure. Zobrazit méně

Hypokinetic dysarthria in Parkinson’s disease (PD) can be characterized by monotony of pitch and loudness, reduced stress, variable rate, imprecise consonants, and a breathy and harsh voice. Using acoustic analysis, we studied the effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) applied over the primary orofacial sensorimotor area (SM1) and the left dorsolateral prefrontal cortex (DLPFC) on motor aspects of voiced speech in PD. Our pilot results indicate that one… Zobrazit více

Hypokinetic dysarthria in Parkinson’s disease (PD) can be characterized by monotony of pitch and loudness, reduced stress, variable rate, imprecise consonants, and a breathy and harsh voice. Using acoustic analysis, we studied the effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) applied over the primary orofacial sensorimotor area (SM1) and the left dorsolateral prefrontal cortex (DLPFC) on motor aspects of voiced speech in PD. Our pilot results indicate that one session of rTMS applied over the SM1 may lead to measurable improvement in voice quality and intensity and an increase in speech rate and tongue movements. Nevertheless, these changes were not accompanied by changes in a perceptual evaluation of speech performance by a speech therapist. Future placebo-controlled studies in larger patient cohorts should verify if rTMS would be clinically useful for treating hypokinetic dysarthria in PD. Zobrazit méně

This paper proposes a novel algorithm for multi-focus thermal image fusion. The algorithm is based on local activity analysis and advanced pre-selection of images into fusion process. The algorithm improves the object temperature measurement error up to 5°C. The proposed algorithm is evaluated by half total error rate, root mean squared error, cross correlation and visual inspection. To the best of our knowledge, this is the first work devoted to multi-focus thermal image fusion. For testing of… Zobrazit více

This paper proposes a novel algorithm for multi-focus thermal image fusion. The algorithm is based on local activity analysis and advanced pre-selection of images into fusion process. The algorithm improves the object temperature measurement error up to 5°C. The proposed algorithm is evaluated by half total error rate, root mean squared error, cross correlation and visual inspection. To the best of our knowledge, this is the first work devoted to multi-focus thermal image fusion. For testing of proposed algorithm we acquire six thermal image set with objects at different focal depth. Zobrazit méně

This work defines a procedure for collecting naturally induced emotional facial expressions through the vision of movie excerpts with high emotional contents and reports experimental data ascertaining the effects of emotions on memory word recognition tasks. The induced emotional states include the four basic emotions of sadness, disgust, happiness, and surprise, as well as the neutral emotional state. The resulting database contains both thermal and visible emotional facial expressions… Zobrazit více

This work defines a procedure for collecting naturally induced emotional facial expressions through the vision of movie excerpts with high emotional contents and reports experimental data ascertaining the effects of emotions on memory word recognition tasks. The induced emotional states include the four basic emotions of sadness, disgust, happiness, and surprise, as well as the neutral emotional state. The resulting database contains both thermal and visible emotional facial expressions, portrayed by forty Italian subjects and simultaneously acquired by appropriately synchronizing a thermal and a standard visible camera. Each subject’s recording session lasted 45 minutes, allowing for each mode (thermal or visible) to collect a minimum of 2000 facial expressions from which a minimum of 400 were selected as highly expressive of each emotion category. The database is available to the scientific community and can be obtained contacting one of the authors. For this pilot study, it was found that emotions and/or emotion categories do not affect individual performance on memory word recognition tasks and temperature changes in the face or in some regions of it do not discriminate among emotional states. Zobrazit méně

When focusing an image, depth of field, aperture and distance from the camera to the object, must be taking into account, both, in visible and in infrared spectrum. Our experiments reveal that in addition, the focusing problem in thermal spectrum is also hardly dependent of the temperature of the object itself (and/or the scene).

The use of biometrics has been successfully applied to security applications for some time. However, the extension of other potential applications with the use of biometric information is a very recent development. This paper summarizes the field of biometrics and investigates the potential of utilizing biometrics beyond the presently limited field of security applications. There are some synergies that can be established within security-related applications. These can also be relevant in other… Zobrazit více

The use of biometrics has been successfully applied to security applications for some time. However, the extension of other potential applications with the use of biometric information is a very recent development. This paper summarizes the field of biometrics and investigates the potential of utilizing biometrics beyond the presently limited field of security applications. There are some synergies that can be established within security-related applications. These can also be relevant in other fields such as health and ambient intelligence. This paper describes these synergies. Overall, this paper highlights some interesting and exciting research areas as well as possible synergies between different applications using biometric information. Zobrazit méně

In this paper, we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7,380 pictures. Experimental results consist of single sensor experiments as well as the combination of two and three sensors under different illumination… Zobrazit více

In this paper, we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7,380 pictures. Experimental results consist of single sensor experiments as well as the combination of two and three sensors under different illumination conditions (natural, infrared and artificial illumination). We have found that the three studied spectral bands contribute in a nearly equal proportion to a combined system. Experimental results show a significant improvement combining the three spectrums, even when using a simple classifier and feature extractor. In six of the nine studied scenarios, we obtained identification rates higher or equal to 98 %, when using a trained combination rule, and two cases of nine when using a fixed rule. Zobrazit méně

In this paper, we propose a novel focus measure that is based on algorithms for interest point detection, particularly on the Fast Hessian detector. The proposed measure is compared with the energy of image gradient and sum-modified Laplacian that are commonly used as focus measures to test its reliability and performance. The measures have been tested on a newly created database containing 84 images (12 images for seven objects). Our algorithm proved to be a good focus measure satisfying all… Zobrazit více

In this paper, we propose a novel focus measure that is based on algorithms for interest point detection, particularly on the Fast Hessian detector. The proposed measure is compared with the energy of image gradient and sum-modified Laplacian that are commonly used as focus measures to test its reliability and performance. The measures have been tested on a newly created database containing 84 images (12 images for seven objects). Our algorithm proved to be a good focus measure satisfying all the requirements described in the paper, in some cases it outperformed the other two measures. Zobrazit méně

Based on the results of our previous fMRI studies we explored effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) applied over the primary orofacial sensorimotor area (SM1) or dorsolateral prefrontal cortex (DLPFC) on paraclinical aspects of voiced speech in PD.

In this paper we present preliminary results about on-line drawings acquired by means of a digitizing tablet, and performed by control population (left and right hand) as well as pathological subjects using their dominant hand. Experimental results reveal a clear difference between both groups, specially on the on-air movements. Although the acquired samples are not enough to extract significant conclusions we think that this preliminary results encourage the experimentation in this research… Zobrazit více

In this paper we present preliminary results about on-line drawings acquired by means of a digitizing tablet, and performed by control population (left and right hand) as well as pathological subjects using their dominant hand. Experimental results reveal a clear difference between both groups, specially on the on-air movements. Although the acquired samples are not enough to extract significant conclusions we think that this preliminary results encourage the experimentation in this research line. Thus, the main purpose of this paper is to attract the attention of the scientific community. Zobrazit méně

Based on results of fMRI studies, we explored effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) of the primary orofacial sensorimotor area (SM1) and dorsolateral prefrontal cortex (DLPFC) on paraclinical aspects of voiced speech in PD using voice signal analysis. Effects of dopaminergic treatment on HD have also been assessed using the same voice analysis and fMRI.

En este artículo se enfatizan las posibilidades del procesamiento de señales biométricas más allá de las aplicaciones de seguridad, con especial énfasis en el ámbito de salud. Se muestra también la interrelación entre la salud y la seguridad. La finalidad es captar la atención de la comunidad biométrica hacia los temas relacionados con la salud, sobre todo en el caso de las enfermedades neurodegenerativas.

This paper is aimed at analysing, from an information theory perspective, the gestures produced by human beings when handwriting a text. Modern capturing devices allow the gathering of data not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. Our past research with isolated uppercase words clearly suggests that both types of trajectories have a biometric potential to perform writer… Zobrazit více

This paper is aimed at analysing, from an information theory perspective, the gestures produced by human beings when handwriting a text. Modern capturing devices allow the gathering of data not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. Our past research with isolated uppercase words clearly suggests that both types of trajectories have a biometric potential to perform writer recognition and that they can be effectively combined to enhance the recognition accuracy. With samples from the BiosecurID database, we have analysed the entropy of each kind of trajectories, as well as the amount of information they share, and the difference between intra- and inter-writer measures of the mutual information. The results show that when pressure is not taken into account, the amount of information is similar in both types of trajectories. Furthermore, even if they share some information, in-air and on-surface trajectories appear to be notably non-redundant. Zobrazit méně

According to some significant advantages, the text-dependent speaker recognition is still widely used in biometric systems. These systems are, in comparison with the text-independent, more accurate and resistant against the replay attacks. There are many approaches regarding the text-dependent recognition. This paper introduces a combination of classifiers based on fractional distances, biometric dispersion matcher and dynamic time warping. The first two mentioned classifiers are based on a… Zobrazit více

According to some significant advantages, the text-dependent speaker recognition is still widely used in biometric systems. These systems are, in comparison with the text-independent, more accurate and resistant against the replay attacks. There are many approaches regarding the text-dependent recognition. This paper introduces a combination of classifiers based on fractional distances, biometric dispersion matcher and dynamic time warping. The first two mentioned classifiers are based on a voice imprint. They have low memory requirements while the recognition procedure is fast. This is advantageous especially in low-cost biometric systems supplied by batteries. It is shown that using the trained score fusion, it is possible to reach successful detection rate equal to 98.98% and 92.19% in case of microphone mismatch. During verification, system reached equal error rate 2.55% and 6.77% when assuming the microphone mismatch. System was tested using Catalan database which consists of 48 speakers (three 3s training samples per speaker). Zobrazit méně

Image segmentation is a first step for most of the biometric systems. In this paper we present a hand image segmentation algorithm for thermographic images. This work is based on a specially acquired database with a thermographic camera TESTO 882-3, which acquires thermal images of 320x240 pixels as well as visible images of 640x480 pixels. Experimental results reveal that thermal images are harder to segment from the background than visible images. Specially for some users. However, thermal… Zobrazit více

Image segmentation is a first step for most of the biometric systems. In this paper we present a hand image segmentation algorithm for thermographic images. This work is based on a specially acquired database with a thermographic camera TESTO 882-3, which acquires thermal images of 320x240 pixels as well as visible images of 640x480 pixels. Experimental results reveal that thermal images are harder to segment from the background than visible images. Specially for some users. However, thermal images provide some interesting information, such as the vein pattern distribution. Zobrazit méně

Patients with Parkinson's disease (PD) usually suffer from hypokinetic dysarthria (HD), which involves impairment of phonation, articulation, prosody, and speech fluency. Our paper deals with parameters that can be used for the evaluation of motor aspects of speech and relevant methods of data acquisition and analysis. A review of specific parameters of HD and methods used for their evaluation may from the practical point of view contribute both to the diagnostic approaches to HD and to the… Zobrazit více

Patients with Parkinson's disease (PD) usually suffer from hypokinetic dysarthria (HD), which involves impairment of phonation, articulation, prosody, and speech fluency. Our paper deals with parameters that can be used for the evaluation of motor aspects of speech and relevant methods of data acquisition and analysis. A review of specific parameters of HD and methods used for their evaluation may from the practical point of view contribute both to the diagnostic approaches to HD and to the development of suitable measures for assessment of its progression. The paper gives a description of the most frequently used parameters and their optimization to enable the best possible automatic classification of the various stages of Parkinson's disease. Zobrazit méně

Lectures describing the basics of digital acoustic signal processing.

In this paper we present a new thermographic image database, suitable for the analysis of automatic focusing measures. This database contains the images of 10 scenes, each of which is represented once for each of 96 different focus positions. Using this database, we evaluate the usefulness of five focus measures with the goal of determining the optimal focus position. Experimental results reveal that the accurate automatic detection of optimal focus position can be achieved with a low… Zobrazit více

In this paper we present a new thermographic image database, suitable for the analysis of automatic focusing measures. This database contains the images of 10 scenes, each of which is represented once for each of 96 different focus positions. Using this database, we evaluate the usefulness of five focus measures with the goal of determining the optimal focus position. Experimental results reveal that the accurate automatic detection of optimal focus position can be achieved with a low computational burden. We also present an acquisition tool for obtaining thermal images. To the best of our knowledge, this is the first study on the automatic focusing of thermal images. Zobrazit méně

In this paper we propose a gender recognition algorithm of face images. We have used PCA and DCT for dimensionality reduction. The algorithm is based on a genetic algorithm to improve the selection of training set of images for the PCA algorithm. Genetic algorithm helps to select the images, which best represent each gender, from the image database. We have evaluated a nearest neighbor classifier as well as a neural network. Experimental results show a correct identification rate of 85,9%.

Patients with Parkinson’s disease (PD) often suffer from hypokinetic dysarthria (HD) involving impairment of phonation, articulation, prosody and speech fluency. Our paper focuses on parameters used to evaluate motor aspects of speech and on methods of relevant data collection and analysis. An overview of specific parameters of HD and methods used for its evaluation may facilitate development of a diagnostic system for HD and of suitable mea­sures for the assessment of its progression.

Although text-dependent speaker recognition systems are not so intensively developed as text-independent ones, there are still many applications, where the text dependency is necessary. This is for example a case of biometric systems. With the increasing computational burden and decreasing price of memories it is possible to implement new and sophisticated algorithms that rapidly increase the accuracy of these systems. But there are still some applications, where fast, simple and efficient… Zobrazit více

Although text-dependent speaker recognition systems are not so intensively developed as text-independent ones, there are still many applications, where the text dependency is necessary. This is for example a case of biometric systems. With the increasing computational burden and decreasing price of memories it is possible to implement new and sophisticated algorithms that rapidly increase the accuracy of these systems. But there are still some applications, where fast, simple and efficient approaches are necessary. This work introduces new methods of the text-dependent speaker recognition based on voice imprint and classifiers suitable for the processing of this feature vector. It is shown, that using combination of voice imprint and classifier based on fractional distances, it is possible to reach the successful detection rate equal to 96.94 % and 82.29 % in case of microphone mismatch. Speaking about equal error rate, system reached 4.08 % and 10.94 % considering microphone mismatch. It is also shown that voice imprint can approximately 30 times decrease the memory requirements. Zobrazit méně

Although the text-dependent recognition is not as universal as the text-independent recognition it has still many applications. Moreover the accuracy of the systems based on the text-dependent recognition is still higher than in case of the text-independent systems. This work proposes new techniques in the field of text-dependent speaker recognition so that it is possible to implement these systems in low-cost devices. It introduces a feature vector called voice imprint, which has low memory… Zobrazit více

Although the text-dependent recognition is not as universal as the text-independent recognition it has still many applications. Moreover the accuracy of the systems based on the text-dependent recognition is still higher than in case of the text-independent systems. This work proposes new techniques in the field of text-dependent speaker recognition so that it is possible to implement these systems in low-cost devices. It introduces a feature vector called voice imprint, which has low memory requirements, and a classifier based on fractional distances which is very fast and accurate in comparison with another classifiers. It is shown that using the fractional distances along with the voice imprint, it is possible to reach successful detection rate equal to 96.94 % and 82.29 % in case of microphone mismatch. During verification, system reached equal error rate 3.93 % and 10.43 % when assuming microphone mismatch. System was tested using database which consists of 48 speakers (three 3 s training samples per speaker). Zobrazit méně

Face segmentation is a first step for face biometric systems. In this paper we present a face segmentation algorithm for thermographic images. This algorithm is compared with the classic Viola and Jones algorithm used for visible images. Experimental results reveal that, when segmenting a multispectral (visible and thermal) face database, the proposed algorithm is more than 10 times faster, while the accuracy of face segmentation in thermal images is higher than in case of Viola-Jones.

In this paper, we propose a criterion for pairwise combination of information from different sensors in order to decide how a given pair of sensors is useful for different applications. This criterion is related to the principle of maximum information preservation. We present experimental results for the case of face images at different spectral bands, which allow for the in advance evaluation of the usefulness of different sensor combinations as well as the possibility for crossed-sensor… Zobrazit více

In this paper, we propose a criterion for pairwise combination of information from different sensors in order to decide how a given pair of sensors is useful for different applications. This criterion is related to the principle of maximum information preservation. We present experimental results for the case of face images at different spectral bands, which allow for the in advance evaluation of the usefulness of different sensor combinations as well as the possibility for crossed-sensor recognition (matching of images acquired in different spectral bands). The criterion that we propose is a generalization of the Fisher score for the case of mutual information, which is measured as the ratio of the interclass information to the intraclass. The score we propose measures the behavior of a pair of sensors either when they are used in combination or when they are used to discriminate between classes. Based on Information Theory measurements, we conclude that the best spectral band combination always contains the thermal image, while the best combination for crossed-sensor recognition is VIS and NIR. Zobrazit méně

Face segmentation is a first step for face biometric systems. In this paper we present a face segmentation algorithm for thermographic images. This algorithm is compared with the classic Viola and Jones algorithm used for visible images, and experimental results reveal that, when segmenting a multispectral (visible and thermal) face database, the proposed algorithm is more than 10 times faster, while the accuracy of face segmentation in thermal images is higher than in case of Viola-Jones.

Although audio-visual human systems have several well-known limitations, artificial sensors can measure information beyond our limits. What would happen if we were able to overcome our limitations? Would we be able to obtain a better knowledge of our environment? Or the information beyond our limits is redundant? In this paper, we compare infrared, thermal and visible images from an information theory point of view. We have acquired a small database and compared several measurements over these… Zobrazit více

Although audio-visual human systems have several well-known limitations, artificial sensors can measure information beyond our limits. What would happen if we were able to overcome our limitations? Would we be able to obtain a better knowledge of our environment? Or the information beyond our limits is redundant? In this paper, we compare infrared, thermal and visible images from an information theory point of view. We have acquired a small database and compared several measurements over these images. While infrasounds and ultrasounds are not directly applicable, for instance, to speaker recognition due to the impossibility of human beings generating sounds in these frequencies, this is not the case with image signals beyond the visible spectrum for face recognition. We have observed that visible, near-infrared and thermal images contain a small amount of redundancy (less than 1,55 bits). Zobrazit méně

The hidden Markov model toolkit (HTK) is set of programs, which are intended to use the hidden Markov models for spontaneous speech recognition. A structure of the HTK and some applications are described in the paper.